eBay:iPads:: startprice.log10 regression:: ebayipads_mdlens_bid1_sp
bdanalytics
Date: (Fri) Oct 16, 2015
Introduction:
Data: Source: Training: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv
New: https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv
Time period:
Synopsis:
Based on analysis utilizing <> techniques,
Regression results: First run:
Classification results: template: prdline.my == “Unknown” -> 296 Low.cor.X.glm: Leaderboard: 0.83458 -> Rank 288 / 1884 0.85514 newobs_tbl=[N=471, Y=327]; submit_filename=template_Final_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=76]=201; max.Accuracy.OOB=0.7710; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=95.42; productline=49.22; D.T.like=29.75; D.T.use=26.32; D.T.box=21.53;
prdline: -> Worse than template prdline.my == “Unknown” -> 285 All.X.no.rnorm.rf: Leaderboard: 0.82649 newobs_tbl=[N=485, Y=313]; submit_filename=prdline_Final_rf_submit.csv OOB_conf_mtrx=[YN=119, NY=80]=199; max.Accuracy.OOB=0.8339; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=84.25; D.sum.TfIdf=7.28; D.T.use=4.26; D.T.veri=2.78; D.T.scratch=1.99; D.T.box=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.81234 newobs_tbl=[N=471, Y=327]; submit_filename=prdline_Low_cor_X_glm_submit.csv OOB_conf_mtrx=[YN=125, NY=74]=199; max.Accuracy.OOB=0.8205; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=96.07; prdline.my=51.37; D.T.like=29.39; D.T.use=25.43; D.T.box=22.27; D.T.veri=; D.T.scratch=;
oobssmpl: -> Low.cor.X.glm: Leaderboard: 0.83402 newobs_tbl=[N=440, Y=358]; submit_filename=oobsmpl_Final_glm_submit OOB_conf_mtrx=[YN=114, NY=84]=198; max.Accuracy.OOB=0.7780; opt.prob.threshold.OOB=0.5 startprice=100.00; biddable=93.87; prdline.my=60.48; D.sum.TfIdf=; D.T.condition=8.69; D.T.screen=7.96; D.T.use=7.50; D.T.veri=; D.T.scratch=;
category: -> Low.cor.X.glm: Leaderboard: 0.82381 newobs_tbl=[N=470, Y=328]; submit_filename=category_Final_glm_submit OOB_conf_mtrx=[YN=119, NY=57]=176; max.Accuracy.OOB=0.8011; opt.prob.threshold.OOB=0.6 startprice=100.00; biddable=79.19; prdline.my=55.22; D.sum.TfIdf=; D.T.ipad=27.05; D.T.like=21.44; D.T.box=20.67; D.T.condition=; D.T.screen=;
dataclns: -> All.X.no.rnorm.rf: Leaderboard: 0.82211 newobs_tbl=[N=485, Y=313]; submit_filename=dataclns_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=75]=179; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=65.85; prdline.my=7.74; D.sum.TfIdf=; D.T.use=2.01; D.T.condition=1.87; D.T.veri=1.62; D.T.ipad=; D.T.like=; Low.cor.X.glm: Leaderboard: 0.79264 newobs_tbl=[N=460, Y=338]; submit_filename=dataclns_Low_cor_X_glm_submit OOB_conf_mtrx=[YN=113, NY=74]=187; max.Accuracy.OOB=0.7977; opt.prob.threshold.OOB=0.5 -> different from prev run of 0.6 biddable=100.00; startprice.log=91.85; prdline.my=38.34; D.sum.TfIdf=; D.T.ipad=29.92; D.T.box=27.76; D.T.work=25.79; D.T.use=; D.T.condition=;
txtterms: -> top_n = c(10) Low.cor.X.glm: Leaderboard: 0.81448 newobs_tbl=[N=442, Y=356]; submit_filename=txtterms_Final_glm_submit OOB_conf_mtrx=[YN=113, NY=69]=182; max.Accuracy.OOB=0.7943; opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=90.11; prdline.my=37.65; D.sum.TfIdf=; D.T.ipad=28.67; D.T.work=24.90; D.T.great=21.44; # [1] “D.T.condit” “D.T.condition” “D.T.good” “D.T.ipad” “D.T.new”
# [6] “D.T.scratch” “D.T.screen” “D.T.this” “D.T.use” “D.T.work”
All.X.glm: Leaderboard: 0.81016
newobs_tbl=[N=445, Y=353]; submit_filename=txtterms_Final_glm_submit
OOB_conf_mtrx=[YN=108, NY=72]=180; max.Accuracy.OOB=0.7966;
opt.prob.threshold.OOB=0.5
biddable=100.00; startprice.log=88.24; prdline.my=33.81; D.sum.TfIdf=;
D.T.scratch=25.51; D.T.use=18.97; D.T.good=16.37; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.great” “D.T.excel” “D.T.work” “D.T.ipad”
Max.cor.Y.rpart: Leaderboard: 0.79258
newobs_tbl=[N=439, Y=359]; submit_filename=txtterms_Final_rpart_submit
OOB_conf_mtrx=[YN=105, NY=76]=181; max.Accuracy.OOB=0.7954802;
opt.prob.threshold.OOB=0.5
startprice.log=100; biddable=; prdline.my=; D.sum.TfIdf=;
D.T.scratch=; D.T.use=; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.80929
newobs_tbl=[N=545, Y=253]; submit_filename=txtterms_Final_rf_submit
OOB_conf_mtrx=[YN=108, NY=61]=169; max.Accuracy.OOB=0.8090395
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=78.82; idseq.my=63.43; prdline.my=45.57;
D.T.use=2.76; D.T.condit=2.35; D.T.scratch=2.00; D.T.good=; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
txtclstr: All.X.no.rnorm.rf: Leaderboard: 0.79363 -> 0.79573 newobs_tbl=[N=537, Y=261]; submit_filename=txtclstr_Final_rf_submit OOB_conf_mtrx=[YN=104, NY=61]=165; max.Accuracy.OOB=0.8135593 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=79.99; idseq.my=64.94; prdline.my=4.14; prdline.my.clusterid=1.15; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
dupobs: All.X.no.rnorm.rf: Leaderboard: 0.79295 newobs_tbl=[N=541, Y=257]; submit_filename=dupobs_Final_rf_submit OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=94.49; idseq.my=67.40; prdline.my=4.48; prdline.my.clusterid=1.99; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.no.rnorm.rf: Leaderboard: 0.79652
newobs_tbl=[N=523, Y=275]; submit_filename=dupobs_Final_rf_submit
OOB_conf_mtrx=[YN=114, NY=65]=179; max.Accuracy.OOB=0.7977401
opt.prob.threshold.OOB=0.5
startprice.log=100.00; biddable=94.24; idseq.my=67.92;
prdline.my=4.33; prdline.my.clusterid=2.17; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
csmmdl: All.X.no.rnorm.rf: Leaderboard: 0.79396 newobs_tbl=[N=525, Y=273]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=111, NY=66]=177; max.Accuracy.OOB=0.8000000 opt.prob.threshold.OOB=0.5 startprice.log=100.00; biddable=90.30; idseq.my=67.06; prdline.my=4.40; cellular.fctr=3.57; prdline.my.clusterid=2.08;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.77867 newobs_tbl=[N=564, Y=234]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=120, NY=53]=173; max.Accuracy.OOB=0.8045198 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=93.99; idseq.my=57.30; prdline.my=9.09; cellular.fctr=3.30; prdline.my.clusterid=2.35;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.77152 newobs_tbl=[N=539, Y=259]; submit_filename=csmmdl_Final_rf_submit OOB_conf_mtrx=[YN=, NY=]=; max.Accuracy.OOB=0.8011299 opt.prob.threshold.OOB=0.5 biddable=100.00; startprice.log=94.93; idseq.my=57.12; prdline.my=9.29; cellular.fctr=3.20; prdline.my.clusterid=2.50; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
All.X.glmnet:
fit_RMSE=???; OOB_RMSE=115.1247; new_RMSE=115.1247;
prdline.my.fctr=100.00; condition.fctrNew=88.53; D.npnct09.log=84.34
biddable=16.48; idseq.my=57.27;spdiff:
All.Interact.X.no.rnorm.rf: Leaderboard: 0.78218 newobs_tbl=[N=517, Y=281]; submit_filename=spdiff_Final_rf_submit OOB_conf_mtrx=[YN=121, NY=38]=159; max.Accuracy.OOB=0.8203390 opt.prob.threshold.OOB=0.6 biddable=100.00; startprice.diff=57.53; idseq.my=41.31; prdline.my=11.43; cellular.fctr=2.36; prdline.my.clusterid=1.82;
All.X.no.rnorm.rf:
fit_RMSE=92.19; OOB_RMSE=130.86; new_RMSE=130.86;
biddable=100.00; prdline.my.fctr=61.92; idseq.my=57.77;
condition.fctr=29.53; storage.fctr=11.22; color.fctr=6.69;
cellular.fctr=6.11
All.X.no.rnorm.rf: Leaderboard: 0.77443
newobs_tbl=[N=606, Y=192]; submit_filename=spdiff_Final_rf_submit
OOB_conf_mtrx=[YN=112, NY=28]=140; max.Accuracy.OOB=0.8418079
opt.prob.threshold.OOB=0.6
startprice.diff=100.00; biddable=96.53; idseq.my=38.10;
prdline.my=3.65; cellular.fctr=2.21; prdline.my.clusterid=0.91; [1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
color: All.Interact.X.glmnet: fit_RMSE=88.64520; prdline.my.fctr:D.TfIdf.sum.stem.stop.Ratio=100.00; prdline.my.fctr:condition.fctr=77.35 D.TfIdf.sum.stem.stop.Ratio=68.18 prdline.my.fctr:color.fctr=68.12 prdline.my.fctr:storage.fctr=63.32
All.X.no.rnorm.rf: Leaderboard: 0.80638
newobs_tbl=[N=550, Y=248]; submit_filename=color_Final_rf_submit
OOB_conf_mtrx=[YN=108, NY=54]=162; max.Accuracy.OOB=0.8169492
opt.prob.threshold.OOB=0.5
biddable=100.00; startprice.diff=77.90; idseq.my=48.49;
D.ratio.sum.TfIdf.nwrds=6.48; storage.fctr=4.74;
D.TfIdf.sum.stem.stop.Ratio=4.57; prdline.my=4.32;[1] “D.T.condit” “D.T.use” “D.T.scratch” “D.T.new” “D.T.good” “D.T.screen” [7] “D.T.ipad” “D.T.great” “D.T.work” “D.T.excel”
assctxt: select_terms: [1] “condit” “use” “scratch” “new” “good” “ipad” “screen” “great”
[9] “work” “excel” “like” “box” “function” “item” “fulli” “minor” [17] “cosmet” “crack” “mint” “wear”
assoc_terms: [1] “bare” “sign” “light” “back” “hous” “tab” “dent”
[8] “brand” “open” “mini” “appl” “air” “wifi” “affect”
[15] “protector” “shape” “perfect” “order” “button” “origin” “retail”
[22] “seal” “includ” “100” “may” “show” “overal” “bodi”
[29] “phone” “will” “damag” “near” “top” “normal” “tear”
[36] “expect” “minim”
glb_allobs_df\(prdline.my\).clusterid Entropy: 0.6665 (97.3037 pct) All.Interact.X.glmnet: fit_RMSE=88.40723; prdline.my.fctr:D.TfIdf.sum.stem.stop.Ratio=100.00; prdline.my.fctriPadAir:D.npnct01.log=79.67748; D.TfIdf.sum.stem.stop.Ratio=79.08192; prdline.my.fctriPadmini 2+:condition.fctrNew other (see details)=78.24020; prdline.my.fctriPad 3+:color.fctrSpace Gray=77.05886; prdline.my.fctriPadmini 2+:storage.fctrUnknown=75.68145; prdline.my.fctrUnknown:.clusterid.fctr3=74.23727;
All.Interact.X.no.rnorm.rf: Leaderboard: 0.72974
newobs_tbl=[N=682, Y=116]; submit_filename=assctxt_Final_rf_submit
OOB_conf_mtrx=[YN=125, NY=43]=168; max.Accuracy.OOB=0.8101695; max.auc.OOB=???;
opt.prob.threshold.OOB=0.6
biddable=100.00; startprice.diff=51.04; idseq.my=29.51;
startprice.diff:biddable=28.70
prdline.my.fctriPadmini:idseq.my=6.89
Highest max.auc.OOB=???; for model: ctgry2: select_terms: 50 assoc_terms: 103 glb_allobs_df\(prdline.my\).clusterid Entropy: 0.6559 (96.7556 pct) All.Interact.X.glmnet: next: All.X.glmnet fit_RMSE=88.80010; prdl.my.descr.fctr:storage.fctr 100.00 prdl.my.descr.fctr:condition.fctr 93.96 prdl.my.descr.fctr:D.npnct01.log 89.94 D.TfIdf.sum.stem.stop.Ratio 75.90 prdl.my.descr.fctr:color.fctr 72.43 prdl.my.descr.fctr:.clusterid.fctr7 63.97 prdl.my.descr.fctr:D.npnct08.log 63.46 prdl.my.descr.fctr 63.05 prdl.my.descr.fctr:D.TfIdf.sum.stem.stop.Ratio 62.91 prdl.my.descr.fctr:D.npnct16.log 62.39
Ensemble.glmnet: Leaderboard: 0.80480
newobs_tbl=[N=473, Y=325]; submit_filename=ctgry2_Final_glmnet_submit
OOB_conf_mtrx=[YN=79, NY=101]=180;
max.Accuracy.OOB=0.7977528; max.auc.OOB=0.8554068; opt.prob.threshold.OOB=0.4
Highest max.auc.OOB=0.8587215; for model:All.X.no.rnorm.rf
biddable 100.000
startprice.diff 71.793
idseq.my 43.511
ensemble: select_terms: 50 assoc_terms: 103 glb_allobs_df\(prdline.my\).clusterid Entropy: 0.6570 (96.9282 pct) Final.glment: min.RMSE.fit=31.45801 Ensemble.glmnet: min.RMSE.fit=30.67172 startprice.predict.All.Interact.X.no.rnorm.rf 100.000 startprice.predict.All.X.no.rnorm.rf 75.381 All.X.glmnet: min.RMSE.fit=88.98066 prdl.my.descr.fctr 100.00 D.TfIdf.sum.stem.stop.Ratio 92.16 condition.fctr 79.01 prdl.my.descr.fctr:.clusterid.fctr5 69.91 D.npnct16.log 61.70 color.fctrWhite 59.42 D.npnct01.log 55.07 cellular.fctr1 53.35 D.terms.n.post.stop 52.92
Ensemble.glmnet: Leaderboard: 0.73183
newobs_tbl=[N=557, Y=241]; submit_filename=ensemble_Final_glmnet_submit
OOB_conf_mtrx=[YN=75, NY=60]=135;
max.Accuracy.OOB=0.8483146; max.auc.OOB=0.9187365;
opt.prob.threshold.OOB=0.5sold.fctr.predict.All.X.no.rnorm.rf.prob 100.000000
sold.fctr.predict.All.Interact.X.no.rnorm.rf.prob 98.873608
Highest max.auc.OOB=0.9180131; for model:All.X.no.rnorm.rf
startprice.diff 100.000 biddable 95.318 idseq.my 33.365
ncv7: select_terms: 50 assoc_terms: 103 glb_allobs_df\(prdline.my\).clusterid Entropy: 0.6570 (96.9282 pct) Final.glment: min.RMSE.fit=31.45704 Ensemble.glmnet: min.RMSE.fit=29.93289 startprice.predict.All.Interact.X.no.rnorm.rf 100.000 startprice.predict.All.X.no.rnorm.rf 82.878 startprice.predict.Low.cor.X.lm 42.664
All.Interact.X.glmnet: min.RMSE.fit=87.30181 prdl.my.descr.fctr:D.npnct01.log 100.00 prdl.my.descr.fctr:condition.fctr 99.98 prdl.my.descr.fctr:storage.fctr 96.26 prdl.my.descr.fctriPadAir 79.65 prdl.my.descr.fctr:color.fctr 79.30 prdl.my.descr.fctr:D.TfIdf.sum.stem.stop.Ratio 74.90 D.TfIdf.sum.stem.stop.Ratio 74.77 prdl.my.descr.fctr:D.npnct08.log 67.38 prdl.my.descr.fctr:D.npnct01.log 67.38
Ensemble.glmnet: Leaderboard: not submitted -> lower max.auc.OOB of "Ensemble submission"
newobs_tbl=[N=561, Y=237]; submit_filename=ncv7_Final_glmnet_submit
OOB_conf_mtrx=[YN=79, NY=55]=134;
max.Accuracy.OOB=0.8494382; max.auc.OOB=0.9130918; opt.prob.threshold.OOB=0.5sold.fctr.predict.All.X.no.rnorm.rf.prob 100.000 sold.fctr.predict.All.Interact.X.no.rnorm.rf.prob 99.348
Highest max.auc.OOB=0.9117720; for model:All.X.no.rnorm.rf
startprice.diff 100.000 biddable 96.383 idseq.my 32.634
gbm w/startprice.unit9: Final.glment: min.RMSE.fit=30.32782 Ensemble.glmnet: min.RMSE.fit=29.62348 startprice.predict.All.Interact.X.no.rnorm.rf 100.000 startprice.predict.All.X.no.rnorm.rf 73.521 startprice.predict.All.Interact.X.bayesglm 29.675 startprice.predict.Max.cor.Y.lm 28.405
All.X.glmnet: min.RMSE.fit=88.64271prdl.my.descr.fctr 100.00 D.TfIdf.sum.stem.stop.Ratio 85.01 condition.fctr 80.28 carrier.fctr 77.48 prdl.my.descr.fctr:.clusterid.fctr5 65.78 D.npnct16.log 61.66 startprice.unit9 59.48 color.fctr 59.21 D.npnct01.log 53.78 D.npnct08.log 53.56 cellular.fctr 53.19
Ensemble.glmnet: Leaderboard: not submitted -> lower max.auc.OOB of "Ensemble submission"
newobs_tbl=[N=579, Y=219]; submit_filename=gbm_Final_glmnet_submit
OOB_conf_mtrx=[YN=85, NY=54]=139;
max.Accuracy.OOB=0.8438202; max.auc.OOB=0.9127314; opt.prob.threshold.OOB=0.5sold.fctr.predict.All.X.no.rnorm.rf.prob 100.0000 sold.fctr.predict.All.Interact.X.no.rnorm.rf.prob 98.7937
Highest max.auc.OOB=0.9167568; for model:All.Interact.X.gbm biddable 100.0000 startprice.diff 96.2076 startprice.diff:biddable 23.2114 idseq.my 7.8098
mdlsel: Final.glment: min.RMSE.fit=30.47114 (higher than gbm w/startprice.unit9) Ensemble.glmnet: min.RMSE.fit=29.49418 startprice.predict.All.Interact.X.no.rnorm.rf 100.000000 startprice.predict.All.X.no.rnorm.rf 71.213880 startprice.predict.All.X.bayesglm 24.166084
All.X.glmnet: min.RMSE.fit=88.64271prdl.my.descr.fctr 100.00 D.TfIdf.sum.stem.stop.Ratio 85.01 condition.fctr 80.28 carrier.fctr 77.48 prdl.my.descr.fctr:.clusterid.fctr5 65.78 D.npnct16.log 61.66 startprice.unit9 59.48 color.fctr 59.21 D.npnct01.log 53.78 D.npnct08.log 53.56 cellular.fctr 53.19
mdlsel(startprice.log): Final.Ensemble.rf: min.RMSE.fit=0.4563772 Ensemble.rf: min.RMSE.fit=0.4283013 startprice.log.predict.All.Interact.X.no.rnorm.rf 100.0000000 startprice.log.predict.All.X.no.rnorm.rf 58.0967582 startprice.log.predict.All.Interact.X.gbm 6.7197148
All.X.no.rnorm.rf: min.RMSE.fit=1.4967021biddable 100.00000000 idseq.my 98.00292371 startprice.unit9 34.31130220 prdl.my.descr.fctr 18.10984741 D.ratio.sum.TfIdf.nwrds 15.23549621 color.fctrUnknown 14.05520993 D.TfIdf.sum.stem.stop.Ratio 13.00884673 D.ratio.nstopwrds.nwrds 10.51165302
All.X.gbm: Leaderboard: 0.75430
newobs_tbl=[N=582, Y=216]; submit_filename=mdlsel_Final_gbm_submit
OOB_conf_mtrx=[YN=58, NY=65]=123;
max.Accuracy.OOB=0.8617978; max.auc.OOB=0.9367161;
opt.prob.threshold.OOB=0.5startprice.diff 100.0000000 100.00000000 biddable 66.6475055 65.40764971 idseq.my 1.8632456 4.55963698
splogdiff: All.X.gbm: Leaderboard: 0.70111 newobs_tbl=[N=553, Y=245]; submit_filename=splogdiff_Final_gbm_submit OOB_conf_mtrx=[YN=35, NY=101]=136; max.Accuracy.OOB=0.8471910; max.auc.OOB=0.9388912; opt.prob.threshold.OOB=0.3 startprice.log.diff 100.0000000 100.0000000 biddable 86.8563123 88.0261866 idseq.my 8.3580281 2.9054298
nofrcdups: All.X.gbm: Leaderboard: ???/0.67225 newobs_tbl=[N=543, Y=255]; submit_filename=nofrcdups_Final_gbm_submit OOB_conf_mtrx=[YN=36, NY=101]=137; opt.prob.threshold.OOB=0.3 max.Accuracy.OOB=0.0.8460674; max.auc.OOB=0.9388582; startprice.log.diff 100.00000000 93.3716491 biddable 83.57786348 100.0000000 idseq.my 11.54696712 1.1240259
nofrcdups w/ glb_sel_mdl_id=All.X.no.rnorm.rf: All.X.no.rnorm.rf: Leaderboard: ???/0.57475 -> ???/0.59937 with force dups newobs_tbl=[N=630, Y=168]; newobs_range_outliers=798; submit_filename=nofrcdups_Final_rf_submit OOB_conf_mtrx=[YN=70, NY=62]=132; OOBobs_range_outliers=27 opt.prob.threshold.OOB=0.5 max.Accuracy.OOB=0.8516854; max.auc.OOB=0.9335308; startprice.log.diff 100.00 biddable 87.61 idseq.my 26.79
Forum Ideas: I then focused on feature engineering, each new variable brought its own little improvement so in the end i just kept adding new ones and let the models do their thing. Here are some i used: model (productline:storage:condition), isNew, model2 (product:isNew), 50 common words from descr, descrLength, capsFactor (% of caps in description), number of cheaper items of same model2, number of dearer items of same model2, priceFactor (vs. mean of price for model), priceFactor2 (vs. mean of price for model2), bigID (if ID> 11000 because there seems to be a huge drop in sales after some time), timeline (year of product launch, reasoning is you want to spend less money on older products).
Get the median startprice for each level of productline and condition. Take the difference from startprice as a new variable. I find median works much better than the mean since startprice is not normally distributed. I also created another binary variable on whether this difference is positive or negative.
Square root startprice
scale and center all the variables except sold, including the dummies.
Prediction Accuracy Enhancement Options:
- Not repeatable experiments:
- gbm & ensembles that include gbm
- rf that includes .rnorm ??? (now .rnorm has a set seed)
- import.data chunk:
- which obs should be in fit vs. OOB (currently dirty.0 vs .1 is split 50%)
- inspect.data chunk:
- For date variables
- Appropriate factors ?
- Different / More last* features ?
- For date variables
- scrub.data chunk:
- transform.data chunk:
- derive features from multiple features
- manage.missing.data chunk:
- Not fill missing vars
- Fill missing numerics with a different algorithm
- Fill missing chars with data based on clusters
- extract.features chunk:
- Text variables: move to date extraction chunk ???
- Mine acronyms
- Mine places
- Text variables: move to date extraction chunk ???
- Review set_global_options chunk after features are finalized
[](.png)
Potential next steps include:
- Organization:
- Categorize by chunk
- Priority criteria:
- Ease of change
- Impacts report
- Cleans innards
- Bug report
- all chunks:
- at chunk-end rm(!glb_)
- manage.missing.data chunk:
- cleaner way to manage re-splitting of training vs. new entity
- extract.features chunk:
- Add n-grams for glb_txt_vars
- “RTextTools”, “tau”, “RWeka”, and “textcat” packages
- Convert user-specified mutate code to config specs
- Add n-grams for glb_txt_vars
- fit.models chunk:
- Prediction accuracy scatter graph:
- Add tiles (raw vs. PCA)
- Use shiny for drop-down of “important” features
Use plot.ly for interactive plots ?
- Change .fit suffix of model metrics to .mdl if it’s data independent (e.g. AIC, Adj.R.Squared - is it truly data independent ?, etc.)
- move model_type parameter to myfit_mdl before indep_vars_vctr (keep all model_* together)
- create a custom model for rpart that has minbucket as a tuning parameter
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
- Probability handling for multinomials vs. desired binomial outcome
- ROCR currently supports only evaluation of binary classification tasks (version 1.0.7)
extensions toward multiclass classification are scheduled for the next release
- Skip trControl.method=“cv” for dummy classifier ?
- Add custom model to caret for a dummy (baseline) classifier (binomial & multinomial) that generates proba/outcomes which mimics the freq distribution of glb_rsp_var values; Right now glb_dmy_glm_mdl always generates most frequent outcome in training data
glm_dmy_mdl should use the same method as glm_sel_mdl until custom dummy classifer is implemented
- fit.all.training chunk:
- myplot_prediction_classification: displays ‘x’ instead of ‘+’ when there are no prediction errors
- Compare glb_sel_mdl vs. glb_fin_mdl:
- varImp
- Prediction differences (shd be minimal ?)
- Move glb_analytics_diag_plots to mydsutils.R: (+) Easier to debug (-) Too many glb vars used
- Add print(ggplot.petrinet(glb_analytics_pn) + coord_flip()) at the end of every major chunk
- Parameterize glb_analytics_pn
- Move glb_impute_missing_data to mydsutils.R: (-) Too many glb vars used; glb_<>_df reassigned
- Replicate myfit_mdl_classification features in myfit_mdl_regression
- Do non-glm methods handle interaction terms ?
- f-score computation for classifiers should be summation across outcomes (not just the desired one ?)
- Add accuracy computation to glb_dmy_mdl in predict.data.new chunk
- Why does splitting fit.data.training.all chunk into separate chunks add an overhead of ~30 secs ? It’s not rbind b/c other chunks have lower elapsed time. Is it the number of plots ?
- Incorporate code chunks in print_sessionInfo
- Test against
- projects in github.com/bdanalytics
- lectures in jhu-datascience track
Analysis:
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mydsutils.R")## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
registerDoMC(6) # # of cores on machine - 2
suppressPackageStartupMessages(require(caret))
source("~/Documents/Work/PullRequests/caret/pkg/caret/R/confusionMatrix.R")
source("~/Documents/Work/PullRequests/caret/pkg/caret/R/ggplot.R")
#packageVersion("tm")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
glb_trnng_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTrain.csv"
glb_newdt_url <- "https://inclass.kaggle.com/c/15-071x-the-analytics-edge-summer-2015/download/eBayiPadTest.csv"
glb_out_pfx <- "ebayipads_mdlens_bid1_sp_"
glb_save_envir <- FALSE # or TRUE
glb_is_separate_newobs_dataset <- TRUE # or TRUE
glb_split_entity_newobs_datasets <- TRUE # or FALSE
glb_split_newdata_method <- "sample" # "condition" or "sample" or "copy"
glb_split_newdata_condition <- NULL # or "is.na(<var>)"; "<var> <condition_operator> <value>"
glb_split_newdata_size_ratio <- 0.3 # > 0 & < 1
glb_split_sample.seed <- 123 # or any integer
glb_obs_drop_condition <- #NULL
"(UniqueID %in% c(NULL
, 11234 #sold=0; 2 other dups(10306, 11503) are sold=1
, 11844 #sold=0; 3 other dups(11721, 11738, 11812) are sold=1
)) | (productline %in% c('iPad 5', 'iPad mini Retina')) |
(biddable != 1)"
#parse(text=glb_obs_drop_condition)
glb_obs_repartition_train_condition <- #NULL
"!is.na(sold) & (sold == 1)"
glb_max_fitobs <- NULL # or any integer
glb_is_regression <- TRUE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE #or FALSE
glb_rsp_var_raw <- "startprice"
# for classification, the response variable has to be a factor
glb_rsp_var <- "startprice.log10" # "sold.fctr" # glb_rsp_var_raw
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- function(raw) { # NULL
# return(raw ^ 0.5)
# return(log(1 + raw))
return(log10(raw))
# return(exp(-raw / 2))
# ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == 1, "Y", "N"); return(relevel(as.factor(ret_vals), ref="N"))
# #as.factor(paste0("B", raw))
# #as.factor(gsub(" ", "\\.", raw))
}
glb_map_rsp_raw_to_var(tst <- c(NA, 0, 2.99, 280.50, 1000.00))## [1] NA -Inf 0.4756712 2.4479329 3.0000000glb_map_rsp_var_to_raw <- function(var) { # NULL #
# return(var ^ 2.0)
# return(exp(var) - 1)
return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var) - 1
# #as.numeric(var)
# #gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# #c(FALSE, TRUE)[as.numeric(var)]
}
glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst))## [1] NA 0.00 2.99 280.50 1000.00if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
glb_rsp_var_out <- paste0(glb_rsp_var, ".predict.") # mdl_id is appended later
# List info gathered for various columns
# <col_name>: <description>; <notes>
# description = The text description of the product provided by the seller.
# biddable = Whether this is an auction (biddable=1) or a sale with a fixed price (biddable=0).
# startprice = The start price (in US Dollars) for the auction (if biddable=1) or the sale price (if biddable=0).
# condition = The condition of the product (new, used, etc.)
# cellular = Whether the iPad has cellular connectivity (cellular=1) or not (cellular=0).
# carrier = The cellular carrier for which the iPad is equipped (if cellular=1); listed as "None" if cellular=0.
# color = The color of the iPad.
# storage = The iPad's storage capacity (in gigabytes).
# productline = The name of the product being sold.
# If multiple vars are parts of id, consider concatenating them to create one id var
# If glb_id_var == NULL, ".rownames <- row.names()" is the default
# Derive a numeric feature from id var
# User-specified exclusions
# List feats that shd be excluded due to known causation by prediction variable
glb_exclude_vars_as_features <- c(NULL
, "description", "productline"
, "sold", "startprice.log10.cut.fctr"
### bid0_sp
# # List feats that are linear combinations (alias in glm)
# , "D.terms.post.stem.n.log", "D.weight.sum"
# #, "prdl.descr.my.fctriPad4#1:.clusterid.fctr3" This does not work
# # if RFE is rated lower than Low.cor, list feats that are in RFE & not in Low.cor
# # min.RMSE.fit(RFE.X.glmnet)=0.1138888
# # D.chrs.n.log 61.12483
# # D.chrs.uppr.n.log 61.12483
# # D.ratio.wrds.stop.n.wrds.n 61.12483
# # D.terms.post.stop.n.log 61.12483
# # D.weight.post.stem.sum 61.12483
# # D.wrds.n.log 61.12483
# # D.wrds.stop.n.log 61.12483
# # D.wrds.unq.n.log 61.12483
# #, "startprice.dcm2.is9" # min.RMSE.fit(RFE.X.glmnet)=0.1141991 (up)
# , "D.wrds.stop.n.log" # min.RMSE.fit(RFE.X.glmnet)=0.1131232
###
)
glb_id_var <- c("UniqueID")
glb_category_var <- "prdl.descr.my.fctr" # "productline" # NULL
glb_drop_vars <- c(NULL) # or c("<col_name>")
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
glb_assign_pairs_lst <- NULL;
# glb_assign_pairs_lst[["<var1>"]] <- list(from=c(NA),
# to=c("NA.my"))
glb_assign_vars <- names(glb_assign_pairs_lst)
# Derived features
glb_derive_lst <- NULL;
# Add logs of numerics that are not distributed normally -> do automatically ???
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1)
# glb_derive_lst[["prdline.my"]] <- list(
# mapfn=function(productline) { return(productline) }
# , args=c("productline"))
glb_derive_lst[["startprice.log10.cut.fctr"]] <- list(
mapfn=function(startprice.log10) { return(cut(startprice.log10, 3)) }
, args=c("startprice.log10"))
glb_derive_lst[["descr.my"]] <- list(
mapfn=function(description) { mod_raw <- description;
# This is here because it does not work with txt_map_filename
mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse="|"), " ",
mod_raw)
# This should go into txt_map_filename
mod_raw <- gsub("\\.\\.", "\\. ", mod_raw);
# Don't parse for "." because of ".com"; use customized gsub for that text
mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Modifications for this exercise only
# Add dictionary to stemDocument e.g. stickers stemmed to sticker ???
mod_raw <- gsub("8\\.25", "825", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 10\\.SCREEN ", " 10\\. SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 128 gb ", " 128gb ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 16G, ", " 16GB, ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 16 gig ", " 16gb ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" 16 gb ", " 16gb ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bAccounts\\b", "Account", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bactivated\\b", "activate", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" actuuly ", " actual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\baffects\\b", "affect", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bApple\\'s", "Apple", mod_raw, ignore.case=FALSE);
#mod_raw <- glb_allobs_df[c(665), txt_var]; mod_raw
mod_raw <- gsub(" Apple care ", " Applecare ", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" ans ", " and ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" bacK!wiped ", " bacK ! wiped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" backplate", " back plate", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bbarley", "barely", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" bend ", " bent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("Best Buy", "BestBuy", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" black\\.Device ", " black \\. Device ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("black\\),charger ", "black\\), charger ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("\\bblacked\\b", "black", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bblemish\\b", "blemishes", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" blocks", " blocked", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" brokenCharger ", " broken Charger ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" perfectlycord ", " perfectly cord ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bcord", "cable", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bcables\\b", "cable", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" care\\.The ", " care\\. The ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(cared|careful|CAREFUL)\\b", "care", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\b(cases|casing)\\b", "case", mod_raw, ignore.case=TRUE);
#mod_raw <- glb_allobs_df[c(88,187,280,1040,1098), txt_var]; mod_raw
mod_raw <- gsub(" carefully ", " careful ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bchargers\\b", "charger", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bchips\\b", "chip", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bcleanly\\b", "clean", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\b(C|c)olor(.*)s\\b", "\\1olor", mod_raw, ignore.case=FALSE);
#mod_raw <- glb_allobs_df[c(280,1411), txt_var]; mod_raw
mod_raw <- gsub("\\bcompletely\\b", "complete", mod_raw, ignore.case=FALSE);
#mod_raw <- glb_allobs_df[c(42,502,676), txt_var]; mod_raw
mod_raw <- gsub("\\b(conditon|condtion|contidion|conditions)\\b", "condition", mod_raw,
ignore.case=TRUE);
#mod_raw <- glb_allobs_df[c(234), txt_var]; mod_raw
mod_raw <- gsub("(CONDITION|ONLY)\\.(\\w)", "\\1\\. \\2", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("(condition)(Has)", "\\1\\. \\2", mod_raw);
mod_raw <- gsub("\\bCONNECTED\\b", "CONNECT", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bconnects\\b", "connect", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" consist ", " consistent ", mod_raw, ignore.case=TRUE);
#mod_raw <- glb_allobs_df[c(195, 379, 437), txt_var]; mod_raw
mod_raw <- gsub("\\bCosmetics\\b", "Cosmetic", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" cracksNo ", " cracks No ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(D|d)amaged\\b", "\\1amage", mod_raw, ignore.case=TRUE);
#mod_raw <- glb_allobs_df[c(116, 1360), txt_var]; mod_raw
mod_raw <- gsub("\\bDays\\b", "Day", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" DEFAULTING ", " DEFAULT ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bdefect(ive)*\\b", "defects", mod_raw, ignore.case=FALSE);
#mod_raw <- glb_allobs_df[c(1403), txt_var]; mod_raw
mod_raw <- gsub(" definitely ", " definite ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(D|d)ented\\b", "\\1ent", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" described", " describe", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" desciption", " description", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" devices", " device", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Digi\\.", " Digitizer\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(ding|dinged)\\b", "dings", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" display\\.New ", " display\\. New ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" displays", " display", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(drop|drops)\\b", "dropped", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(E|e)dge\\b", "\\1dges", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" effect ", " affect ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Excellant ", " Excellent ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" excellently", " excellent", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" EUC ", " excellent used condition", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" feels ", " feel ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" fineiCloud ", " fine iCloud ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" fine.Its ", " fine. Its ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bfix\\b", "fixed", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bflaws\\b", "flaw", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bflawlessly\\b", "flawless", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Framing ", " Frame ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" functioanlity", " functionality", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bfunction(ing|ality)\\b", "functional", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" functional\\.Very little ", " functional\\. Very little ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("^Gentle ", "Gently ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\(gray color", "\\(spacegray color", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" GREAT\\.SCreen ", " GREAT\\. SCreen ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("\\b(guarantee|guarantees)\\b", "guaranteed", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\ba handful of times\\b", "sparingly", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bhardly any\\b", "no", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bhardly ever used\\b", "sparingly used", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("iCL0UD", "iCLOUD", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("----I cloud ", " ----Icloud ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("^iPad Black 3rd generation ", "iPad 3 Black ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" IMEINo ", " IMEI No ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bincluding\\b", "included", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" install\\. ", " installed\\. ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("inivisible", "invisible", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^I pad ", "Ipad ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(lock|locks)\\b", "locked", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\blots\\b", "lot", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" manuals ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" book ", " manual ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" mars ", " marks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" marks\\.Absolutely ", " marks\\. Absolutely ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub("\\bmarkings\\b", "marks", mod_raw, ignore.case=TRUE);
#mod_raw <- glb_allobs_df[c(200, 1301), txt_var]; mod_raw
mod_raw <- gsub(" minimum", " minimal", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" MINT\\.wiped ", " MINT\\. wiped ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bmonth\\b", "months", mod_raw, ignore.case=TRUE);
#mod_raw <- glb_allobs_df[c(29, 38, 194, 511, 789, 819), txt_var]; mod_raw
mod_raw <- gsub(" NEW\\!(SCREEN|ONE) ", " NEW\\! \\1 ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" new looking$", " looks new", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" newer ", " new ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bnoted\\b", "note", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" oped ", " opened ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" opening", " opened", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" operated", " operational", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("otter box", "otterbox", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bpackage\\b", "packaging", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bPACKAGE\\b", "PACKAGing", mod_raw, ignore.case=FALSE);
#mod_raw <- glb_allobs_df[c(360, 1142), txt_var]; mod_raw
mod_raw <- gsub(" performance", " performs", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" personalized ", " personal ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bPhysically\\b", "Physical", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\b(picture|pictured)\\b", "pictures", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bPICTURE\\b", "PICTUREs", mod_raw, ignore.case=FALSE);
#mod_raw <- glb_allobs_df[c(184, 892), txt_var]; mod_raw
mod_raw <- gsub("\\b[P|p]ower(ed|ing|s)\\b", "\\1ower", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" pre- owned ", " used ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bprevious\\b", "previously", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bpreviously (owned|used)\\b", "used", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bproblem\\b", "problems", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" products ", " product ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bprotected\\b", "protector", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bprotection\\b", "protector", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bPROTECTION\\b", "PROTECTOR", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" Keeped ", " Kept ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" knicks ", " nicks ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" lightening ", " lightning ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bminis\\b", "mini", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("^READiPad ", "READ iPad ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" re- assemble ", " reassemble ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" REFURB\\.", " REFURBISHED\\.", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" reponding", " respond", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" rotation ", " rotate ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Sales ", " Sale ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(scratchs|scratching)\\b", "scratches", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" SCREEB ", " SCREEN ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(S|s)creens\\b", "\\1creen", mod_raw, ignore.case=TRUE);
#mod_raw <- glb_allobs_df[c(83, 489), txt_var]; mod_raw
mod_raw <- gsub("\\bset\\b", "settings", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" shipped| Shipment", " ship", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bshowing\\b", "shows", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("shrink wrap", "shrinkwrap", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bshuts\\b", "shut", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" sides ", " side ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" skinned,", " skin,", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bSlightly\\b", "slight", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bspace (grey|gray)", "spacegray", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" spec ", " speck ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("^somescratches ", "some scratches ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Sticker ", " Stickers ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bstoring", "store", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("SWAPPA\\.COM", "SWAPPAsdotCOM", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" T- Mobile", " TMobile", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\b(tear|TEAR)(s|S)\\b", "\\1", mod_raw, ignore.case=FALSE);
#mod_raw <- glb_allobs_df[c(376), txt_var]; mod_raw
mod_raw <- gsub(" touchscreen ", " touch screen ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bTURN\\b", "TURNS", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" UnlockedCracked ", " Unlocked Cracked ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bUNUSABLE\\b", "UNUSED", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\b(update|updates)\\b", "updated", mod_raw, ignore.case=FALSE);
mod_raw <- gsub("\\bupgrade\\b", "upgraded", mod_raw, ignore.case=FALSE);
mod_raw <- gsub(" uppser ", " upper ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" use\\.Scratches ", " use\\. Scratches ", mod_raw,
ignore.case=TRUE);
mod_raw <- gsub(" verify ", " verified ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" wear\\.Device ", " wear\\. Device ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub("\\bwears\\b", "\\wear", mod_raw, ignore.case=TRUE);
#mod_raw <- glb_allobs_df[c(167, 272), txt_var]; mod_raw
mod_raw <- gsub(" whats ", " what's ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" WiFi\\+4G ", " WiFi \\+ 4G ", mod_raw, ignore.case=TRUE);
mod_raw <- gsub(" Zaag Invisible Shield", " Zaag InvisibleShield", mod_raw,
ignore.case=TRUE);
return(mod_raw) }
, args=c("description"))
# mapfn=function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn=function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn=function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn=function(Week) { return(substr(Week, 1, 10)) }
# mapfn=function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# , args=c("raw"))
# mapfn=function(PTS, oppPTS) { return(PTS - oppPTS) }
# , args=c("PTS", "oppPTS"))
# # If glb_allobs_df is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glb_allobs_df)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glb_derive_lst[["<txt_var>.niso8859.log"]] <- list(
# mapfn=function(<txt_var>) { match_lst <- gregexpr("&#[[:digit:]]{3};", <txt_var>)
# match_num_vctr <- unlist(lapply(match_lst,
# function(elem) length(elem)))
# return(log(1 + match_num_vctr)) }
# , args=c("<txt_var>"))
# mapfn=function(raw) { mod_raw <- raw;
# mod_raw <- gsub("&#[[:digit:]]{3};", " ", mod_raw);
# # Modifications for this exercise only
# mod_raw <- gsub("\\bgoodIn ", "good In", mod_raw);
# return(mod_raw)
# # Create user-specified pattern vectors
# #sum(mycount_pattern_occ("Metropolitan Diary:", glb_allobs_df$Abstract) > 0)
# if (txt_var %in% c("Snippet", "Abstract")) {
# txt_X_df[, paste0(txt_var_pfx, ".P.metropolitan.diary.colon")] <-
# as.integer(0 + mycount_pattern_occ("Metropolitan Diary:",
# glb_allobs_df[, txt_var]))
#summary(glb_allobs_df[ ,grep("P.on.this.day", names(glb_allobs_df), value=TRUE)])
# glb_allobs_df$<descriptor>.my <-
# plyr::revalue(glb_allobs_df$<descriptor>.my, c(
# "ABANDONED BUILDING" = "OTHER",
# "##" = "##"
# ))
# print(<descriptor>_freq_df <- mycreate_sqlxtab_df(glb_allobs_df, c("<descriptor>.my")))
# # print(dplyr::filter(<descriptor>_freq_df, grepl("(MEDICAL|DENTAL|OFFICE)", <descriptor>.my)))
# # print(dplyr::filter(dplyr::select(glb_allobs_df, -<var.zoo>),
# # grepl("STORE", <descriptor>.my)))
# glb_derive_lst[["<var1>"]] <- glb_derive_lst[["<var2>"]]
glb_derive_vars <- names(glb_derive_lst)
# tst <- "descr.my"; args_lst <- NULL; for (arg in glb_derive_lst[[tst]]$args) args_lst[[arg]] <- glb_allobs_df[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glb_derive_lst[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glb_date_vars <- NULL # or c("<date_var>")
glb_date_fmts <- list(); #glb_date_fmts[["<date_var>"]] <- "%m/%e/%y"
glb_date_tzs <- list(); #glb_date_tzs[["<date_var>"]] <- "America/New_York"
#grep("America/New", OlsonNames(), value=TRUE)
# Text Processing Step: custom modifications not present in txt_munge
glb_txt_vars <- c("descr.my") # NULL #
Sys.setlocale("LC_ALL", "C") # For english## [1] "C/C/C/C/C/en_US.UTF-8"# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "ebay_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: removePunctuation (use custom transformer to replace with space ???)
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words; Check stemming of "significant" words - any stopped words that should be stemmed with them ?
if (!is.null(glb_txt_vars)) {
require(tm)
glb_txt_stop_words[["descr.my"]] <- sort(c(NULL
, setdiff(removePunctuation(stopwords("english")), "no")
,"ac"
# cor.y.train == NA
,unlist(strsplit(paste(c(NULL
,"128gb,1st,32gb,3g,64gb,90,acceptable,activation,amount,average,bad,buttons,buy,came,camera,can,care,carrier"
#,casing
,"certified,charge,charging,cleaned,clear,come,components,contain,corner,correctly,covered,customer,earbuds"
,"engraved,engraving,engravement" # somehow didn't show up in the cor.y.train == NA list
,"entire,except,fair,features,feel,fine,generation,get,gift,got,heavily,heavy,however,imei,include,inspected,invisible,invisibleshield"
,"ipad,ipads"
,"issues"
#,items,
,"keyboard,lightning,listing,little,looks,lower"
,"manufacture,manufacturer"# somehow didn't show up in the cor.y.train == NA list
,"meaning,model,near,need,needs,nicks,opened,operational,otherwise"
,"person,personal"# somehow didn't show up in the cor.y.train == NA list
,"phone,photos,pics,plastic,port,professionally"
,"purchased,purchasing"# somehow didn't show up in the cor.y.train == NA list
,"quality,questions,read,ready"
,"receive,received"# somehow didn't show up in the cor.y.train == NA list
,"removed,replaced,retail,return,returns,runs"
#,scratch,
,"scuffing,sealed,sell,seller,selling,shape,ship,shown,silver,since,sold,sound,spacegray,stock,sync,tablet,taken,technician,tests,third,time,touch,units,unlocked,week,wifi,without"
,"wrap" # somehow didn't show up in the cor.y.train == NA list
,"zagg"
), collapse=",")
, "[,]")) #err.abs.fit.sum=26.869473 w/o items,scratch
# cor.y.abs is low
#,"always","comes","grade","moderate","protector"
))
} ## Loading required package: tm
## Loading required package: NLP
##
## Attaching package: 'NLP'
##
## The following object is masked from 'package:ggplot2':
##
## annotate# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txt_var]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txt_var]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txt_var]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+term, subset(glb_post_stem_words_terms_df_lst[[txt_var]], !is.na(cor.y))), 5)
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txt_var]]), 20)
#mydsp_obs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glb_allobs_df[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glb_category_var, "storage", txt_var)]
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txt_var]][grep("^moder", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txt_var]][grep("^came$", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txt_var]][glb_allobs_df$.lcn == "Fit", term_row_df$pos], glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(cluster_vars)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txt_var]][grep("condit", glb_post_stop_words_terms_df_lst[[txt_var]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^p", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
#glb_allobs_df[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glb_id_var, "productline", txt_var)]
#glb_allobs_df[which(TfIdf_stem_mtrx[, 191] > 0), c(glb_id_var, glb_category_var, txt_var)]
#which(glb_allobs_df$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txt_var]][grep("^c", glb_post_stem_words_terms_df_lst[[txt_var]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glb_allobs_df$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txt_var]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glb_txt_corpus_lst[[txt_var]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glb_allobs_df$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glb_allobs_df$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glb_trnobs_df[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glb_txt_synonyms <- list()
glb_txt_synonyms[["descr.my"]] <- #NULL #: default
list(NULL
, list(word="cabl", syns=c("cabl", "cord"))#err.abs.fit.sum=26.863220
# , list(word="charger", syns=c("charg", "charger"))
# , list(word="come", syns=c("came", "come"))
# , list(word="dent", syns=c("dent", "ding"))
# , list(word="damag", syns=c(#"bad", "blemish", "broken", "crack",
# #defect has +ve cor, others have -ve cor
# "damag", "dent", "ding",
# #"scratch", "scuff", "tear", "wear",
# NULL))
# # combining damag with defect & dent results in higher err.abs.fit.sum=26.885899
# # combining defect with dent in higher err.abs.fit.sum=26.894976
# , list(word="defect", syns=c(#"bad", "blemish", "broken", "crack",
# "defect", "dent", #"ding", ding has -ve cor, others have +ve cor
# #"scratch", "scuff", "tear", "wear",
# NULL))
#, list(word="new", syns=c("brand")) ???
# , list(word="scuff", syns=c("scuf", "scuff"))
# , list(word="show", syns=c("show", "shown"))
# , list(word="tablet", syns=c("tab", "tablet"))
)
if (length(glb_txt_synonyms) > 0) names(glb_txt_synonyms) <- glb_txt_vars
# Text Processing Step: filterTerms
if (!is.null(glb_txt_vars)) {
require(tm)
# options include: weightTf, myweightTflog1p, myweightTfsqrt, weightTfIdf, weightBM25
glb_txt_terms_control <- list(weighting=weightTfIdf
# termFreq selection criteria across obs: default: list(global=c(1, Inf))
, bounds=list(global=c(3, Inf))
# termFreq selection criteria for each obs: default: list(local=c(1, Inf))
#, bounds=list(local=c(2, Inf))
# default: c(3, Inf)
, wordLengths = c(2, Inf)
)
}
glb_txt_cor_var <- "startprice.log10.cut.fctr" # glb_rsp_var
# select one from c("union.top.val.cor", "top.cor", default: "top.val", "sparse")
glb_txt_terms_filter <- "top.val"
glb_txt_top_n <- c(20) # 50 in non_sp
names(glb_txt_top_n) <- glb_txt_vars
# Text Processing Step: extractAssoc
glb_txt_assoc_cor <- c(0.4) # 0.2 in non_sp
names(glb_txt_assoc_cor) <- glb_txt_vars
# Text Processing Step: extractPatterns (ngrams)
# Potential Enhancements
# "Seller refurbished" -> D.P.refurbished.seller ?
# "Like new" -> D.P.new.like ?
# "No scratches" -> D.P.scratch.no ?
glb_important_terms <- list()
# Remember to use stemmed terms
# Have to set it even if it is not used
glb_sprs_thresholds <- c(0.950) # Generates 8 terms
# Properties:
# numrows(glb_feats_df) << numrows(glb_fitobs_df)
# Select terms that appear in at least 0.2 * O(FP/FN(glb_OOBobs_df))
# numrows(glb_OOBobs_df) = 1.1 * numrows(glb_newobs_df)
names(glb_sprs_thresholds) <- glb_txt_vars
glb_price_vars <- c("startprice") # NULL or c("<price_var>")
if (glb_rsp_var_raw != glb_rsp_var)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_rsp_var_raw)
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glb_cluster <- TRUE # default:FALSE
glb_cluster.seed <- 189 # or any integer
# c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glb_cluster_entropy_var <- "sold" #"startprice.log10.cut.fctr"
glb_exclude_cluster_vars_as_features <- TRUE # default FALSE
glb_interaction_only_feats_lst <- list()
glb_interaction_only_feats_lst[["carrier.fctr"]] <- "cellular.fctr"
glb_nzv_freqCut <- 19 # 19 is caret default
glb_nzv_uniqueCut <- 4 # 10 is caret default
# outliers identified by car::outlierTest
glb_obsfit_outliers <- c(NULL # default: NULL
# biddable == 0 & 1; err.abs.fit.sum=423.55172
# # outliers
# , 10813 # next 665 w/ rstudent=-5.091080; biddable=3.263257; err.abs.fit.sum=418.598755
# , 10666 # next 1727 w/ rstudent=-5.163517; biddable=4.293465; err.abs.fit.sum=414.093609
# , 11736 # next 780 w/ rstudent=-5.181343; biddable=5.670483; err.abs.fit.sum=401.817992
# # old biddable importance above this
# , 10781 # next 1323 w/ rstudent=-5.151062; biddable=13.30602; err.abs.fit.sum=396.393721
# #, 10091 # next 91 w/ rstudent=-4.444452; biddable=; err.abs.fit.sum=402.673715 (up)
# #, 10166 # next 560 w/ rstudent=-5.006795; biddable=; err.abs.fit.sum=401.759324 (up)
# #, 10281 # next 281 w/ rstudent=-4.245087; biddable=; err.abs.fit.sum=401.316926 (up)
# #, 10285 # next 285 w/ rstudent=-4.483190; biddable=; err.abs.fit.sum=402.608936 (up)
# #, 10446 # next 445 w/ rstudent=-4.663418; biddable=; err.abs.fit.sum=403.074523 (up)
# #, 10542 # next 1323 w/ rstudent=-5.214517; biddable=; err.abs.fit.sum=401.04205 (up)
# #, 10543 # next 1323 w/ rstudent=-5.214517; biddable=; err.abs.fit.sum=401.04205 (up)
# #, 10561 # next 542 w/ rstudent=-4.736154; biddable=; err.abs.fit.sum=401.56198 (up)
# #, 10631 # next 166 w/ rstudent=-5.073048; biddable=; err.abs.fit.sum=401.556788 (up)
# #, 11330 # next 630 w/ rstudent=-5.117659; biddable=; err.abs.fit.sum=401.732597 (up)
# , 10091, 10166, 10281, 10285, 10446, 10542, 10543, 10561, 10631, 11330
# # biddable=18.93923; err.abs.fit.sum=359.388769
# , 10330 #biddable=19.06084; err.abs.fit.sum=355.895702
# , 10402 #biddable= 0.0 ; err.abs.fit.sum=351.315181
# , 10438 #biddable= 0.0 ; err.abs.fit.sum=347.821527
# , 10624 #biddable= 0.0 ; err.abs.fit.sum=343.724904
# , 10659 #biddable= 0.0 ; err.abs.fit.sum=331.873603
# , 11323 #biddable=10.45901; err.abs.fit.sum=324.929562
# , 11422 #biddable= 0.0 ; err.abs.fit.sum=334.839805 (up)
# biddable == 0; err.abs.fit.sum=26.713317
# , 11448 # outliers; next is 858 w/ rstudent=-5.855132; err.abs.fit.sum=24.212800
# , 11583 # outliers; next is 856 w/ rstudent=-4.792849; err.abs.fit.sum=22.164035
# , 11581 # outliers; next is 743 w/ rstudent=-4.005054; err.abs.fit.sum=18.842901
# , 10837 # outliers; next is 336 w/ rstudent=-5.279215; err.abs.fit.sum=18.124560
# , 11442 # outliers; next is 904 w/ rstudent=-4.474844; err.abs.fit.sum=15.533211
# , 11697 # outliers; next is 874 w/ rstudent=-3.678664; err.abs.fit.sum=13.829375
# , 10799 # .hatvalues == 1; total 8; iPadmini#1; err.abs.fit.sum=13.807283
# #, 10017 # .hatvalues == 1; total 7; iPad3#1; err.abs.fit.sum=14.620782 (up)
# #, 10027, 10859 # .hatvalues == 1; total 7; iPad1#1; err.abs.fit.sum=14.570246 (up)
# , 10332 # .hatvalues == 1; total 7; iPad4#1; err.abs.fit.sum=13.706467
# , 11759 # .hatvalues == 1; total 6; iPadAir2#1; err.abs.fit.sum=13.643043
# , 10675 # .hatvalues == 1; total 5; iPadAir#1; err.abs.fit.sum=13.623787
# #, 11119 # .hatvalues == 1; total 4; iPadmini3#1; err.abs.fit.sum=NA
# , 10017, 10027, 10859 # .hatvalues == 1; total 1; iPad3#1 & iPad1#1; err.abs.fit.sum=13.438903
# biddable == 1; err.abs.fit.sum=361.78243
, 10813 # outliers; next is 665 w/ rstudent=-5.021180; err.abs.fit.sum=356.83424
, 10666 # outliers; next is 808 w/ rstudent=-4.764126; err.abs.fit.sum=352.46437
, 11736 # outliers; next is 665 w/ rstudent=-4.614022; err.abs.fit.sum=348.59977
, 10542 # outliers; next is 665 w/ rstudent=-4.654923; err.abs.fit.sum=344.18546
, 11330 # outliers; next is 327 w/ rstudent=-4.628972; err.abs.fit.sum=336.12636
, 10561 # outliers; next is 56 w/ rstudent=-4.612970; err.abs.fit.sum=329.50309
, 10166 # outliers; next is 318 w/ rstudent=-4.717238; err.abs.fit.sum=318.50562
, 10543 # outliers; next is 464 w/ rstudent=-4.811116; err.abs.fit.sum=314.32801
, 10285 # outliers; next is 21 w/ rstudent=-4.850822; err.abs.fit.sum=310.19008
#, 10091 # outliers; next is 464 w/ rstudent=-4.941448; err.abs.fit.sum=312.94069 (up)
#, 10781 # outliers; next is 250 w/ rstudent=-4.793502; err.abs.fit.sum=313.03867 (up)
, 10446 # outliers; next is 371 w/ rstudent=-4.787578; err.abs.fit.sum=307.15681
, 10631 # outliers; next is 165 w/ rstudent=-4.130356; err.abs.fit.sum=303.34549
#, 10330 # outliers; next is 217 w/ rstudent=-4.067684; err.abs.fit.sum=312.75121 (up)
#, 10402 # outliers; next is 388 w/ rstudent=-4.067684; err.abs.fit.sum=311.84516 (up)
#, 10659 # outliers; next is 128 w/ rstudent=-3.982911; err.abs.fit.sum=311.84516 (up)
, 10091, 10781, 10330, 10402, 10659#, 10281 outliers; err.abs.fit.sum=282.381827; iPad4#0=13.806011; iPad4#1=7.799398
#, 10281 # outliers; next is NA w/ rstudent=NA; err.abs.fit.sum=287.147331 (up); iPad4#0=14.372770; iPad4#1=4.591408
#, 10624 # outliers; ignored along with 10281 err.abs.fit.sum=289.116467 (up); iPad4#0=; iPad4#1=
#, 10624 # outliers; ignored w/o 10281 err.abs.fit.sum=286.415040 (up); iPad4#0=; iPad4#1=
#, 10636 # hatvalues==1; next is 11652; err.abs.fit.sum=290.50254 (up)
, 11652 # hatvalues==1; next is 10636; err.abs.fit.sum=282.183867
#err.abs.fit.sum=282.227249
)
# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#print(outliers <- car::outlierTest(glb_models_lst[["RFE.X.glm"]]$finalModel))
#print(outliers_df <- data.frame(.Bonf.p=outliers$bonf.p))
#model_diags_df <- cbind(glb_fitobs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[["RFE.X.glm"]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[["RFE.X.glm"]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[["RFE.X.glm"]]$finalModel)))
#print(subset(model_diags_df, is.na(.dffits)))
#print(subset(model_diags_df, .hatvalues == 1))
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glb_fitobs_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))
#indep_vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(glb_sel_mdl_id, ".importance"))), glb_featsimp_df))); indep_vars <- indep_vars[!grepl(".fctr", indep_vars, fixed=TRUE)]
#myplot_parcoord(obs_df=model_diags_df[, c(glb_id_var, glb_category_var, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:20])], obs_ix=row.names(model_diags_df) %in% names(outliers$rstudent)[1], id_var=glb_id_var, category_var=glb_category_var)
#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glb_category_var] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glb_id_var, glb_category_var, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glb_id_var, category_var=glb_category_var)
#table(glb_fitobs_df[model_diags_df[, glb_category_var] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glb_fitobs_df[model_diags_df[, glb_category_var] %in% c("iPad1#1"), c(glb_id_var, "startprice")]
# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glb_id_var, glb_category_var, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glb_id_var, category_var=glb_category_var)
#dffits_ctgry_df <- subset(dffits_df, prdl.descr.my.fctr %in% c("Unknown#0"))
#myplot_parcoord(obs_df=dffits_ctgry_df[, c(glb_id_var, glb_category_var, ".dffits", ".Bonf.p", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indep_vars[1:5])], obs_ix=seq(1:nrow(dffits_ctgry_df))[!is.na(dffits_ctgry_df$.Bonf.p)], id_var=glb_id_var, category_var=glb_category_var)
#
#car::influenceIndexPlot(glb_models_lst[["RFE.X.glm"]]$finalModel, id.n=3)
myplot_parcoord <- function (obs_df, obs_ix=1:nrow(obs_df), id_var=".rownames", category_var=NULL) {
# Setup id_df & remove id_var from range computation
if (id_var != ".rownames") {
id_df <- obs_df[obs_ix, id_var, FALSE]
obs_df <- obs_df[, setdiff(names(obs_df), id_var), FALSE]
} else id_df <- data.frame(.rownames=row.names(obs_df)[obs_ix])
# Setup category_var -> Create a facet ???
category_df <- id_df
if (is.null(category_var)) {
category_var <- ".category"; category_df[, category_var] <- as.factor(0)
} else {
category_df[, category_var] <- obs_df[obs_ix, category_var]
obs_df <- obs_df[, setdiff(names(obs_df), category_var), FALSE]
}
ranges_mtrx <- apply(obs_df, 2L, range, na.rm = TRUE)
obs_scld_df <- as.data.frame(apply(obs_df, 2L,
function(feat) { feat_rng <- max(feat, na.rm = TRUE) - min(feat, na.rm = TRUE);
feat_rng <- ifelse(feat_rng == 0, 1, feat_rng);
return((feat - min(feat, na.rm = TRUE)) / feat_rng) }))
obsT_df <- as.data.frame(t(obs_df))
names(obsT_df) <- paste(".obs", names(obsT_df), sep=".");
obsT_df$.var.name <- row.names(obsT_df)
obsT_df$.var.pos <- 1:length(row.names(obsT_df))
obsST_df <- as.data.frame(t(obs_scld_df))
names(obsST_df) <- paste(".obs", names(obsST_df), sep=".");
obsST_df$.var.name <- row.names(obsST_df)
obsST_df$.var.pos <- 1:length(row.names(obsST_df))
plt_violin_df <- tidyr::gather(obsST_df, key=obs, value=value, -.var.name, -.var.pos)
obsHST_df <- as.data.frame(t(obs_scld_df[obs_ix, ]));
names(obsHST_df) <- as.character(id_df[, id_var])
obsHST_df$.var.name <- row.names(obsHST_df)
obsHST_df$.var.pos <- 1:length(row.names(obsHST_df))
#plt_df <- tidyr::gather(xt_df, key=obs, value=value, -c(.var.name, .var.pos))
# plt_df <- tidyr::gather_(xt_df, key=interp(id_var), value="value", quote(-c(.var.name, .var.pos)))
plt_obsHST_df <- tidyr::gather_(obsHST_df, key=interp(id_var), value="value",
-grep("(\\.var\\.name|\\.var\\.pos)", names(obsHST_df)))
ranges_df <- cbind(as.data.frame(ranges_mtrx), data.frame(.type=c("min", "max")))
ranges_df <- tidyr::gather(ranges_df, key=.var, value=value, -.type)
ranges_df$.y <- ifelse(ranges_df$.type == "min", -0.05, 1.05)
ranges_df <- merge(ranges_df, obsT_df[, c(".var.name", ".var.pos")],
by.x=".var", by.y=".var.name", all.x=TRUE)
ranges_df$.x <- ranges_df$.var.pos
ranges_df <- subset(ranges_df, select=-.var.pos)
plt_obsHST_df <- merge(plt_obsHST_df, category_df, x.all=TRUE)
# plt_obsHST_df[, category_var] <- NA
# plt_obsHST_df[plt_obsHST_df[, id_var] == 11448, glb_category_var] <- "Unknown#0"
# plt_obsHST_df[plt_obsHST_df[, id_var] == 11581, glb_category_var] <- "iPad4#1"
# plt_obsHST_df[plt_obsHST_df[, id_var] == 11583, glb_category_var] <- "Unknown#0"
gp <- ggplot(plt_obsHST_df, aes(x=reorder(.var.name, .var.pos), y=value)) +
geom_violin(data=plt_violin_df, aes(x=reorder(.var.name, .var.pos), y=value),
color="grey80", scale="width") +
geom_line(data=plt_obsHST_df,
aes_string(group=id_var, color=id_var, linetype=category_var), size=1) +
geom_point(data=plt_obsHST_df, aes_string(shape=category_var), size=3) +
scale_color_brewer(type="qual", palette="Set1") +
geom_vline(xintercept=1:length(names(obs_df)), color="grey50") +
geom_text(data=ranges_df,
aes_string(x=".x", y=".y", label="myformat_number(value)"),
size=3.5) +
theme(axis.text.x=element_text(hjust=1, angle=45),
axis.text.y=element_blank(), axis.ticks.y=element_blank()) +
xlab("") + ylab("")
# ggtitle("Dummy")
return(gp)
}
# myplot_parcoord(obs_df=glb_fitobs_df[, c(glb_id_var, glb_rsp_var,
# "startprice.log10.predict.RFE.X.glmnet",
# indep_vars[1:5])], obs_ix=hatobs_ix, id_var=glb_id_var)
# myplot_parcoord(x=glb_fitobs_df[, c(glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet",
# indep_vars[1:2])], obs_ix=hatobs_ix)
# hatvals <- hatvalues(glb_models_lst[["RFE.X.glm"]]$finalModel)
# hatobs_ix <- which(hatvals == max(hatvals))
# MASS::parcoord(x=glb_fitobs_df[, c(glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet",
# indep_vars[1:2])], var.label=TRUE)
#plot(hatvalues(glb_models_lst[["RFE.X.glm"]]$finalModel), type = "h")
#glb_fitobs_df[which(row.names(glb_fitobs_df) %in% c("972")), c(glb_id_var, glb_rsp_var, glb_rsp_var_raw, "sold", glb_category_var)]
#all.equal(glb_models_lst[[glb_sel_mdl_id]], glb_models_lst[[glb_fin_mdl_id]])
glb_obstrn_outliers <- c(glb_obsfit_outliers
)
#car::outlierTest(glb_models_lst[["RFE.X.glm"]]$finalModel)
#glb_trnobs_df[which(row.names(glb_fitobs_df) %in% c("972")), c(glb_id_var, glb_rsp_var, glb_rsp_var_raw, "sold", glb_category_var)]
glb_rfe_fit_sizes <- ### bid0_sp
# c(106, 111, 116, 120, 128) # or NULL c(8, 16, 32, 64, 128, 140)
### bid1_sp
c(8, 11, 16, 21, 32, 64, 128)
glb_models_lst <- list(); glb_models_df <- data.frame()
# Regression
if (glb_is_regression) {
glb_mdl_methods <- c(NULL
# deterministic
#, "lm",
, "glm"
#, "bayesglm" # crashing w/ parallel processing
, "glmnet", "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # predicts 1 for all obs in bid0_sp # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
#, "svmPoly" # crashing w/ parallel processing #, "svmPoly" runs 75 models per cv sample for tunelength=5
#, "svmRadial" # crashing w/ parallel processing
, "earth", "bagEarth" # Takes a long time
#, "parRF" # crashing w/ parallel processing
)
} else
# Classification - Add ada,bagEarth (auto feature selection)
if (glb_is_binomial)
glb_mdl_methods <- c("glm", "bayesglm", "glmnet", "rpart", "rf", "gbm") else
glb_mdl_methods <- c("rpart", "rf", "gbm")
glb_mdl_family_lst <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
# methods: Choose from c(NULL, <method>, glb_mdl_methods)
glb_mdl_family_lst[["RFE.X"]] <- glb_mdl_methods
### bid1_sp
# glb_mdl_family_lst[["CSM.X"]] <- "glmnet"
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# # from RFE.X
# , "startprice.dgt1.is9", "startprice.dcm2.is9", "startprice.dcm1.is9", "startprice.dgt2.is9"
# #, "condition.fctr"
# , "prdl.descr.my.fctr", "color.fctr"
# #, "D.ratio.weight.sum.wrds.n"
# , "cellular.fctr", "cellular.fctr:carrier.fctr"
#
# # from RFE.X.Interact
# , "cellular.fctr:prdl.descr.my.fctr", "cellular.fctr:startprice.dgt2.is9", "cellular.fctr:startprice.dgt1.is9", "cellular.fctr:color.fctr"
# , "cellular.fctr:condition.fctr" # RMSE up with keeping condition.fctr in the model
# # RMSE & R.sq up with removing condition.fctr from the model
# , "cellular.fctr:D.ratio.weight.sum.wrds.n"
# )
### bid1_sp
glb_mdl_family_lst[["All.X"]] <- "glmnet"
glb_mdl_family_lst[["Best.Interact"]] <- "glmnet"
# Check if interaction features make fit better
# Check if tuning parameters make fit better
glb_tune_models_df <- data.frame()
#RFE.X.avNNet
### bid0_sp
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
### bid1_sp
# size=[1] 3 5 7 9; decay=0 0.0001 [0.001] 0.01 0.1; bag=[FALSE]; RMSE=0.9285472
### bid0&1_sp
#RFE.X.bagEarth
### bid0_sp
#RFE.X.bagEarth degree=[1]; nprune=[33]; RMSE=0.1507259
### bid1_sp
#RFE.X.bagEarth degree=[1]; nprune=[32]; RMSE=0.6379639
#RFE.X.bagEarth degree=[1] 2 3; nprune=8 16 32 64 [128]; RMSE=0.6334405
#RFE.X.bagEarth degree=1 [2]; nprune=16 32 64 128 [256]; RMSE=0.6211924
#RFE.X.bagEarth degree=1 [2]; nprune=64 128 200 225 [256]; RMSE=0.6320776 (up)
#RFE.X.bagEarth degree=[1] 2; nprune=64 128 225 256 [275]; RMSE=0.640644 (up)
#RFE.X.bagEarth degree=1 [2] 3; nprune=64 128 200 [256] 300; RMSE=0.6496039 (up)
#RFE.X.bagEarth degree=1 [2] 3; nprune=32 64 128 256 [512]; RMSE=0.6404529 (up)
#RFE.X.bagEarth degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
))
### bid0&1_sp
### bid0_sp
#RFE.X.earth degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
### bid0_sp
#RFE.X.gbm
### bid0_sp
# shrinkage=[0.1]; n.trees=50 100 150 [200] 250; RMSE=0.2062651
# shrinkage=0.00 0.05 0.10 0.15 [0.20]; n.trees=50 [100] 150 200 250; interaction.depth=1 [2] 3 4 5; n.minobsinnode=[10]; RMSE=0.2019453
# shrinkage=0.00 0.05 [0.10] 0.15 0.20; n.trees=50 100 150 200 [250]; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method="gbm", parameter="shrinkage", min=0.05, max=0.25, by=0.05)
# ,data.frame(method="gbm", parameter="n.trees", min=100, max=300, by=50)
# ,data.frame(method="gbm", parameter="interaction.depth", min=1, max=5, by=1)
# ,data.frame(method="gbm", parameter="n.minobsinnode", min=10, max=10, by=10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
### bid1_sp
# shrinkage=[0.1]; n.trees=50 100 150 200 [250]; interaction.depth=1 2 3 4 [5]; n.minobsinnode=[10]; RMSE=0.5054172
# shrinkage=0.03 [0.04] 0.05 0.06 0.07; n.trees=100 [150] 200 250 300; interaction.depth=2 3 4 5 [6]; n.minobsinnode=6 [8] 10 12 14; RMSE=0.5036430
# shrinkage=0.03 [0.04] 0.05 0.06 0.07; n.trees=100 150 [200] 250 300; interaction.depth=3 4 5 [6] 7; n.minobsinnode=6 8 [10] 12 14; RMSE=0.502774
# shrinkage=0.04; n.trees=200; interaction.depth=6; n.minobsinnode=10; RMSE=0.502774
# shrinkage=[0.05] 0.10 0.15 0.20 0.25; n.trees=100 [150] 200 250 300; interaction.depth=2 3 [4] 5 6; n.minobsinnode=[10]; RMSE=0.5058678 (up)
glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
,data.frame(method = "gbm", parameter = "shrinkage", vals = "0.04")
,data.frame(method = "gbm", parameter = "n.trees", vals = "200")
,data.frame(method = "gbm", parameter = "interaction.depth", vals = "6")
,data.frame(method = "gbm", parameter = "n.minobsinnode", vals = "10")
))
### bid0&1_sp
#RFE.X.glmnet
### bid1_sp
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
### bid1_sp
#RFE.X.nnet
### bid0_sp
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; RMSE=1.3300906
### bid1_sp
# size=1 3 5 7 [9]; decay=0e+00 1e-04 1e-03 1e-02 [1e-01]; RMSE=0.9289109
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
))
### bid0&1_sp
#RFE.X.rf
### bid0_sp
# mtry=2 35 [68] 101 134; RMSE=0.1331992
# mtry=2 35 68 [101] 134; RMSE=0.1339974
### bid0_sp
#RFE.X.rpart
### bid0_sp
# cp=[0.03230142] 0.06012801 0.09395662 0.12251081 0.35258370; RMSE=0.1771138
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method="rpart", parameter="cp", min=0.02, max=0.04, by=0.005)
# ))
### bid1_sp
# cp=[0.008081388] 0.016191995 0.027590245 0.299848193 0.361621486; RMSE=0.5294398
# cp=[0.005] 0.006 0.007 0.008 0.009 0.010; RMSE=0.522678
# cp=0.001 [0.003] 0.005 0.007 0.009; RMSE=0.5186586
glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
,data.frame(method="rpart", parameter="cp", min=0.001, max=0.010, by=0.002)
))
### bid0&1_sp
#RFE.X.svmLinear
### bid0_sp
# C=[1]; RMSE=0.1374094
# C=1e-02 [0.1] 5e-01 1e+00 2e+00 3e+00 4e+00 1e+01 1e+02; RMSE=0.1271318
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
### bid1_sp
# C=[1]; RMSE=0.6614060
# C=1e-02 [1e-01] 1e+00 1e+01 1e+02; RMSE=0.6373977
# C=[0.05] 0.10 0.50 1.00 10.00; RMSE=0.6324697
# C=0.01 [0.05] 0.10 0.50 1.00; RMSE=0.6324697
glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
))
### bid0&1_sp
#RFE.X.svmLinear2
### bid0_sp
# cost=[0.25] 0.50 1.00 2.00 4.00; RMSE=0.1276354
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
### bid1_sp
# cost=[0.25] 0.50 1.00 2.00 4.00; RMSE=0.6483622
# cost=[0.0625] 0.1250 0.25 0.50 1.00; RMSE=0.6335311
# cost=0.0312 [0.0625] 0.1250 0.25 0.50; RMSE=0.6335311
glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0312 0.0625 0.125 0.25 0.50")
))
### bid0&1_sp
#RFE.X.svmPoly
### bid0_sp
# degree=[1] 2 3; scale=0.001 0.01 [0.1] 1 10; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1276130
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ))
# glb_tune_models_df <- myrbind_df(glb_tune_models_df, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
### bid0_sp
#RFE.X.svmRadial
### bid0_sp
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
### bid0_sp
#data.frame(parameter="mtry", min=080, max=100, by=10),
#glb_to_sav(); all.equal(sav_models_df, glb_models_df)
#glb_models_df <- subset(sav_models_df, id != "RFE.X.gbm"); print(sort(glb_models_df$id))
glb_preproc_methods <- #NULL
### bid0_sp
# c("YeoJohnson", "center.scale",
# # crashes with train: all the RMSE metric values are missing
# # probably due to interaction vars
# "range", "pca", "ica",
# "spatialSign")
### bid0_sp
### bid1_sp
c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")
### bid1_sp
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<col_name>")
glb_model_metric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glb_model_metric <- NULL # or "<metric_name>"
glb_model_metric_maximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glb_model_metric_smmry <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glb_model_metric_terms)
# metric <- sum(confusion_mtrx * glb_model_metric_terms) / nrow(data)
# names(metric) <- glb_model_metric
# return(metric)
# }
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
#glb_model_evl_criteria <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit")
glb_model_evl_criteria <- c("min.RMSE.fit", "max.Adj.R.sq.fit", "max.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glb_model_evl_criteria <-
c("max.auc.OOB", "max.Accuracy.OOB", "max.Kappa.OOB", "min.aic.fit") else
glb_model_evl_criteria <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <-
### bid0_sp
# c("RFE.X.glm"
# #, "RFE.X.bayesglm"
# , "RFE.X.glmnet", "RFE.X.rpart", "RFE.X.gbm", "RFE.X.rf", "RFE.X.svmLinear", "RFE.X.svmLinear2"
# #, "RFE.X.svmPoly", "RFE.X.svmRadial"
# , "RFE.X.earth", "RFE.X.bagEarth", "RFE.X.Interact.glmnet", "RFE.X.YeoJohnson.glmnet", "RFE.X.center.scale.glmnet", "RFE.X.spatialSign.glmnet")
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
c("RFE.X.spatialSign.rf", "RFE.X.YeoJohnson.rf", "RFE.X.center.scale.rf", "RFE.X.rf", "RFE.X.avNNet", "RFE.X.bagEarth", "RFE.X.earth", "RFE.X.gbm", "RFE.X.glmnet", "RFE.X.nnet", "RFE.X.svmLinear2", "RFE.X.glm", "RFE.X.svmLinear", "RFE.X.rpart")
### bid1_sp
glb_sel_mdl_id <- NULL #select from c(NULL, "RFE.X.glmnet")
glb_fin_mdl_id <- NULL #select from c(NULL, glb_sel_mdl_id)
glb_dsp_cols <- c("sold", ".grpid", "color", "condition", "cellular", "carrier", "storage")
glb_out_obs <- "all" # select from c(NULL, "all", "new", "trn")
glb_out_vars_lst <- list()
# glb_id_var will be the first output column, by default
#glb_out_vars_lst[[glb_id_var]] <- glb_id_var
glb_out_vars_lst[[glb_rsp_var_raw]] <- glb_rsp_var_raw
glb_out_vars_lst[[paste0(head(unlist(strsplit(glb_rsp_var_out, "")), -1), collapse = "")]] <-
"%<d-% paste0(glb_rsp_var_out, glb_fin_mdl_id)"
# Depict process
glb_analytics_pn <- petrinet(name="glb_analytics_pn",
trans_df=data.frame(id=1:6,
name=c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df=data.frame(
begin=c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end =c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL, "import.data")## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 9.07 NA NAStep 1.0: import data
chunk option: eval=
#glb_chunks_df <- myadd_chunk(NULL, "import.data")
glb_to_sav <- function() {
sav_allobs_df <<- glb_allobs_df
sav_trnobs_df <<- glb_trnobs_df
if (any(grepl("glb_fitobs_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_fitobs_df)) sav_fitobs_df <<- glb_fitobs_df
if (any(grepl("glb_OOBobs_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_OOBobs_df)) sav_OOBobs_df <<- glb_OOBobs_df
if (any(grepl("glb_newobs_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_newobs_df)) {
#print("Attempting to save glb_newobs_df...")
sav_newobs_df <<- glb_newobs_df
}
if (any(grepl("glb_ctgry_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_ctgry_df)) sav_ctgry_df <<- glb_ctgry_df
if (!is.null(glb_models_lst )) sav_models_lst <<- glb_models_lst
if (!is.null(glb_models_df )) sav_models_df <<- glb_models_df
if (any(grepl("glb_feats_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_feats_df)) sav_feats_df <<- glb_feats_df
if (any(grepl("glb_featsimp_df", ls(envir=globalenv()), fixed=TRUE)) &&
!is.null(glb_featsimp_df)) sav_featsimp_df <<- glb_featsimp_df
}
glb_trnobs_df <- myimport_data(url=glb_trnng_url, comment="glb_trnobs_df",
force_header=TRUE)## [1] "Reading file ./data/eBayiPadTrain.csv..."
## [1] "dimensions of data in ./data/eBayiPadTrain.csv: 1,861 rows x 11 cols"
## description
## 1 iPad is in 8.5+ out of 10 cosmetic condition!
## 2 Previously used, please read description. May show signs of use such as scratches to the screen and
## 3
## 4
## 5 Please feel free to buy. All products have been thoroughly inspected, cleaned and tested to be 100%
## 6
## biddable startprice condition cellular carrier color
## 1 0 159.99 Used 0 None Black
## 2 1 0.99 Used 1 Verizon Unknown
## 3 0 199.99 Used 0 None White
## 4 0 235.00 New other (see details) 0 None Unknown
## 5 0 199.99 Seller refurbished Unknown Unknown Unknown
## 6 1 175.00 Used 1 AT&T Space Gray
## storage productline sold UniqueID
## 1 16 iPad 2 0 10001
## 2 16 iPad 2 1 10002
## 3 16 iPad 4 1 10003
## 4 16 iPad mini 2 0 10004
## 5 Unknown Unknown 0 10005
## 6 32 iPad mini 2 1 10006
## description
## 65
## 283 Pristine condition, comes with a case and stylus.
## 948 \211\333\317Used Apple Ipad 16 gig 1st generation in Great working condition and 100% functional.Very little
## 1354
## 1366 Item still in complete working order, minor scratches, normal wear and tear but no damage. screen is
## 1840
## biddable startprice condition cellular carrier color
## 65 0 195.00 Used 0 None Unknown
## 283 1 20.00 Used 0 None Unknown
## 948 0 110.00 Seller refurbished 0 None Black
## 1354 0 300.00 Used 0 None White
## 1366 1 125.00 Used Unknown Unknown Unknown
## 1840 0 249.99 Used 1 Sprint Space Gray
## storage productline sold UniqueID
## 65 16 iPad mini 0 10065
## 283 64 iPad 1 0 10283
## 948 32 iPad 1 0 10948
## 1354 16 iPad Air 1 11354
## 1366 Unknown iPad 1 1 11366
## 1840 16 iPad Air 1 11840
## description
## 1856 Overall item is in good condition and is fully operational and ready to use. Comes with box and
## 1857 Used. Tested. Guaranteed to work. Physical condition grade B+ does have some light scratches and
## 1858 This item is brand new and was never used; however, the box and/or packaging has been opened.
## 1859
## 1860 This unit has minor scratches on case and several small scratches on the display. \nIt is in
## 1861 30 Day Warranty. Fully functional engraved iPad 1st Generation with signs of normal wear which
## biddable startprice condition cellular carrier
## 1856 0 89.50 Used 1 AT&T
## 1857 0 239.95 Used 0 None
## 1858 0 329.99 New other (see details) 0 None
## 1859 0 400.00 New 0 None
## 1860 0 89.00 Seller refurbished 0 None
## 1861 0 119.99 Used 1 AT&T
## color storage productline sold UniqueID
## 1856 Unknown 16 iPad 1 0 11856
## 1857 Black 32 iPad 4 1 11857
## 1858 Space Gray 16 iPad Air 0 11858
## 1859 Gold 16 iPad mini 3 0 11859
## 1860 Black 64 iPad 1 1 11860
## 1861 Black 64 iPad 1 0 11861
## 'data.frame': 1861 obs. of 11 variables:
## $ description: chr "iPad is in 8.5+ out of 10 cosmetic condition!" "Previously used, please read description. May show signs of use such as scratches to the screen and " "" "" ...
## $ biddable : int 0 1 0 0 0 1 1 0 1 1 ...
## $ startprice : num 159.99 0.99 199.99 235 199.99 ...
## $ condition : chr "Used" "Used" "Used" "New other (see details)" ...
## $ cellular : chr "0" "1" "0" "0" ...
## $ carrier : chr "None" "Verizon" "None" "None" ...
## $ color : chr "Black" "Unknown" "White" "Unknown" ...
## $ storage : chr "16" "16" "16" "16" ...
## $ productline: chr "iPad 2" "iPad 2" "iPad 4" "iPad mini 2" ...
## $ sold : int 0 1 1 0 0 1 1 0 1 1 ...
## $ UniqueID : int 10001 10002 10003 10004 10005 10006 10007 10008 10009 10010 ...
## - attr(*, "comment")= chr "glb_trnobs_df"
## NULL# glb_trnobs_df <- read.delim("data/hygiene.txt", header=TRUE, fill=TRUE, sep="\t",
# fileEncoding='iso-8859-1')
# glb_trnobs_df <- read.table("data/hygiene.dat.labels", col.names=c("dirty"),
# na.strings="[none]")
# glb_trnobs_df$review <- readLines("data/hygiene.dat", n =-1)
# comment(glb_trnobs_df) <- "glb_trnobs_df"
# glb_trnobs_df <- data.frame()
# for (symbol in c("Boeing", "CocaCola", "GE", "IBM", "ProcterGamble")) {
# sym_trnobs_df <-
# myimport_data(url=gsub("IBM", symbol, glb_trnng_url), comment="glb_trnobs_df",
# force_header=TRUE)
# sym_trnobs_df$Symbol <- symbol
# glb_trnobs_df <- myrbind_df(glb_trnobs_df, sym_trnobs_df)
# }
# glb_trnobs_df <-
# glb_trnobs_df %>% dplyr::filter(Year >= 1999)
if (glb_is_separate_newobs_dataset) {
glb_newobs_df <- myimport_data(url=glb_newdt_url, comment="glb_newobs_df",
force_header=TRUE)
# To make plots / stats / checks easier in chunk:inspectORexplore.data
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df);
comment(glb_allobs_df) <- "glb_allobs_df"
} else {
glb_allobs_df <- glb_trnobs_df; comment(glb_allobs_df) <- "glb_allobs_df"
if (!glb_split_entity_newobs_datasets) {
stop("Not implemented yet")
glb_newobs_df <- glb_trnobs_df[sample(1:nrow(glb_trnobs_df),
max(2, nrow(glb_trnobs_df) / 1000)),]
} else if (glb_split_newdata_method == "condition") {
glb_newobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=glb_split_newdata_condition)))
glb_trnobs_df <- do.call("subset",
list(glb_trnobs_df, parse(text=paste0("!(",
glb_split_newdata_condition,
")"))))
} else if (glb_split_newdata_method == "sample") {
require(caTools)
set.seed(glb_split_sample.seed)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=(1-glb_split_newdata_size_ratio))
glb_newobs_df <- glb_trnobs_df[!split, ]
glb_trnobs_df <- glb_trnobs_df[split ,]
} else if (glb_split_newdata_method == "copy") {
glb_trnobs_df <- glb_allobs_df
comment(glb_trnobs_df) <- "glb_trnobs_df"
glb_newobs_df <- glb_allobs_df
comment(glb_newobs_df) <- "glb_newobs_df"
} else stop("glb_split_newdata_method should be %in% c('condition', 'sample', 'copy')")
comment(glb_newobs_df) <- "glb_newobs_df"
myprint_df(glb_newobs_df)
str(glb_newobs_df)
if (glb_split_entity_newobs_datasets) {
myprint_df(glb_trnobs_df)
str(glb_trnobs_df)
}
} ## [1] "Reading file ./data/eBayiPadTest.csv..."
## [1] "dimensions of data in ./data/eBayiPadTest.csv: 798 rows x 10 cols"
## description
## 1 like new
## 2 Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though
## 3 This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer.
## 4
## 5 Grade A condition means that the Ipad is 100% working condition. Cosmetically 8/9 out of 10 - Will
## 6 Brand new factory sealed iPad in an OPEN BOX...THE BOX ITSELF IS HEAVILY DISTRESSED(see
## biddable startprice condition cellular carrier color
## 1 0 105.00 Used 1 AT&T Unknown
## 2 0 195.00 Used 0 None Unknown
## 3 0 219.99 Used 0 None Unknown
## 4 1 100.00 Used 0 None Unknown
## 5 0 210.99 Manufacturer refurbished 0 None Black
## 6 0 514.95 New other (see details) 0 None Gold
## storage productline UniqueID
## 1 32 iPad 1 11862
## 2 16 iPad mini 2 11863
## 3 64 iPad 3 11864
## 4 16 iPad mini 11865
## 5 32 iPad 3 11866
## 6 64 iPad Air 2 11867
## description
## 1 like new
## 142 iPad mini 1st gen wi-fi 16gb is in perfect working order.
## 309 In excellent condition. Minor scratches on the back. Screen in mint condition. Comes in original
## 312 iPad is in Great condition, the screen is in great condition showing only a few minor scratches, the
## 320 Good condition and fully functional
## 369
## biddable startprice condition cellular carrier color storage
## 1 0 105.00 Used 1 AT&T Unknown 32
## 142 1 0.99 Used 0 None Unknown 16
## 309 0 200.00 Used 1 AT&T Black 32
## 312 1 0.99 Used 0 None Unknown 16
## 320 1 60.00 Used 0 None White 16
## 369 1 197.97 Used 0 None Unknown 64
## productline UniqueID
## 1 iPad 1 11862
## 142 iPad mini 12003
## 309 iPad 3 12170
## 312 iPad mini 2 12173
## 320 iPad 1 12181
## 369 iPad mini 3 12230
## description
## 793 Crack on digitizer near top. Top line of digitizer does not respond to touch. Other than that, all
## 794
## 795
## 796
## 797
## 798 Slightly Used. Includes everything you need plus a nice leather case!\nThere is a slice mark on the
## biddable startprice condition cellular carrier color
## 793 0 104.00 For parts or not working 1 Unknown Black
## 794 0 95.00 Used 1 AT&T Unknown
## 795 1 199.99 Manufacturer refurbished 0 None White
## 796 0 149.99 Used 0 None Unknown
## 797 0 7.99 New Unknown Unknown Unknown
## 798 0 139.00 Used 1 Unknown Black
## storage productline UniqueID
## 793 16 iPad 2 12654
## 794 64 iPad 1 12655
## 795 16 iPad 4 12656
## 796 16 iPad 2 12657
## 797 Unknown iPad 3 12658
## 798 32 Unknown 12659
## 'data.frame': 798 obs. of 10 variables:
## $ description: chr "like new" "Item is in great shape. I upgraded to the iPad Air 2 and don't need the mini any longer, even though " "This iPad is working and is tested 100%. It runs great. It is in good condition. Cracked digitizer." "" ...
## $ biddable : int 0 0 0 1 0 0 0 0 0 1 ...
## $ startprice : num 105 195 220 100 211 ...
## $ condition : chr "Used" "Used" "Used" "Used" ...
## $ cellular : chr "1" "0" "0" "0" ...
## $ carrier : chr "AT&T" "None" "None" "None" ...
## $ color : chr "Unknown" "Unknown" "Unknown" "Unknown" ...
## $ storage : chr "32" "16" "64" "16" ...
## $ productline: chr "iPad 1" "iPad mini 2" "iPad 3" "iPad mini" ...
## $ UniqueID : int 11862 11863 11864 11865 11866 11867 11868 11869 11870 11871 ...
## - attr(*, "comment")= chr "glb_newobs_df"
## NULLif ((num_nas <- sum(is.na(glb_trnobs_df[, glb_rsp_var_raw]))) > 0)
stop("glb_trnobs_df$", glb_rsp_var_raw, " contains NAs for ", num_nas, " obs")
if (nrow(glb_trnobs_df) == nrow(glb_allobs_df))
warning("glb_trnobs_df same as glb_allobs_df")
if (nrow(glb_newobs_df) == nrow(glb_allobs_df))
warning("glb_newobs_df same as glb_allobs_df")
if (length(glb_drop_vars) > 0) {
warning("dropping vars: ", paste0(glb_drop_vars, collapse=", "))
glb_allobs_df <- glb_allobs_df[, setdiff(names(glb_allobs_df), glb_drop_vars)]
glb_trnobs_df <- glb_trnobs_df[, setdiff(names(glb_trnobs_df), glb_drop_vars)]
glb_newobs_df <- glb_newobs_df[, setdiff(names(glb_newobs_df), glb_drop_vars)]
}
#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Combine trnent & newobs into glb_allobs_df for easier manipulation
glb_trnobs_df$.src <- "Train"; glb_newobs_df$.src <- "Test";
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, ".src")
glb_allobs_df <- myrbind_df(glb_trnobs_df, glb_newobs_df)
comment(glb_allobs_df) <- "glb_allobs_df"
# Check for duplicates in glb_id_var
if (length(glb_id_var) == 0) {
warning("using .rownames as identifiers for observations")
glb_allobs_df$.rownames <- rownames(glb_allobs_df)
glb_trnobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Train"))
glb_newobs_df$.rownames <- rownames(subset(glb_allobs_df, .src == "Test"))
glb_id_var <- ".rownames"
}
if (sum(duplicated(glb_allobs_df[, glb_id_var, FALSE])) > 0)
stop(glb_id_var, " duplicated in glb_allobs_df")
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_id_var)
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_trnobs_df <- glb_newobs_df <- NULL
# For Tableau
write.csv(glb_allobs_df, "data/eBayiPadAll.csv", row.names=FALSE)
#stop(here"); glb_to_sav()
# Make any data corrections here
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "cellular"] <- "1"
glb_allobs_df[glb_allobs_df[, glb_id_var] == 10986, "carrier"] <- "T-Mobile"
# if (!is.null(glb_drop_obs_condition))
# glb_allobs_df <- do.call("subset",
# list(glb_allobs_df, parse(text=paste0("!(", glb_drop_obs_condition, ")"))))
# - Merge glb_obs_stack_condition & glb_obs_drop_condition
# - Derive glb_obs_stack|drop_chk_vars from condition automatically
# - Implement glb_obs_stack_condition & glb_obs_stack_chk_vars options
dsp_partition_stats <- function(obs_df, vars=NULL) {
lcl_vars <- NULL
for (var in c(vars, glb_rsp_var_raw)) {
if ((length(unique(obs_df[, var])) > 5) && is.numeric(obs_df[, var])) {
cut_var <- paste0(var, ".cut.fctr")
obs_df[, cut_var] <- cut(obs_df[, var], 3)
lcl_vars <- union(lcl_vars, cut_var)
} else lcl_vars <- union(lcl_vars, var)
}
print("Partition stats:")
print(mycreate_sqlxtab_df(obs_df, union(lcl_vars, ".src")))
for (var in lcl_vars) {
print(freq_df <- mycreate_sqlxtab_df(obs_df, union(var, ".src")))
print(myplot_hbar(freq_df, ".src", ".n", colorcol_name=var))
}
print(mycreate_sqlxtab_df(obs_df, ".src"))
# if (length(unique(glb_allobs_df[, glb_rsp_var_raw])) > 5) {
# cut_var <- paste0(glb_rsp_var_raw, ".cut.fctr")
# glb_allobs_df[, cut_var] <- cut(glb_allobs_df[, glb_rsp_var_raw], 3)
# glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, cut_var)
# glb_obs_stack_chk_vars <- union(cut_var, glb_obs_stack_chk_vars)
# } else glb_obs_stack_chk_vars <- union(glb_rsp_var_raw, glb_obs_stack_chk_vars)
# #glb_obs_stack_chk_vars <- union(glb_obs_stack_chk_vars, ".src")
# print(mycreate_sqlxtab_df(glb_allobs_df, union(var, ".src")))
# print(mycreate_sqlxtab_df(glb_allobs_df, union(glb_obs_stack_chk_vars, ".src")))
# for (var in glb_obs_stack_chk_vars) {
# print(mycreate_sqlxtab_df(glb_allobs_df, union(var, ".src")))
# }
# print(mycreate_sqlxtab_df(glb_allobs_df, ".src"))
}
myget_symbols <- function(txt) {
#print(getParseData(parse(text=txt, keep.source=TRUE)))
return(unique(subset(getParseData(parse(text=txt, keep.source=TRUE)),
token == "SYMBOL")$text))
}
# tokens <- unlist(strsplit(gsub("[[:punct:]|[:space:]]", " ", glb_obs_drop_condition), " "))
# tokens <- tokens[tokens != ""]
# glb_obs_drop_chk_vars <- c("biddable") # or NULL
dsp_partition_stats(obs_df=glb_allobs_df, vars=myget_symbols(glb_obs_drop_condition))## [1] "Partition stats:"## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk## UniqueID.cut.fctr productline biddable startprice.cut.fctr
## 1 (1e+04,1.09e+04] iPad 2 1 (-0.99,333]
## 2 (1e+04,1.09e+04] iPad 1 1 (-0.99,333]
## 3 (1.18e+04,1.27e+04] iPad 2 0 (-0.99,333]
## 4 (1.09e+04,1.18e+04] iPad 2 0 (-0.99,333]
## 5 (1e+04,1.09e+04] iPad mini 1 (-0.99,333]
## 6 (1.09e+04,1.18e+04] iPad mini 0 (-0.99,333]
## 7 (1.18e+04,1.27e+04] iPad 2 1 (-0.99,333]
## 8 (1.09e+04,1.18e+04] Unknown 0 (-0.99,333]
## 9 (1.18e+04,1.27e+04] iPad mini 1 (-0.99,333]
## 10 (1e+04,1.09e+04] iPad 3 1 (-0.99,333]
## 11 (1.09e+04,1.18e+04] iPad mini 1 (-0.99,333]
## 12 (1.09e+04,1.18e+04] iPad 1 0 (-0.99,333]
## 13 (1e+04,1.09e+04] iPad 2 0 (-0.99,333]
## 14 (1.09e+04,1.18e+04] iPad Air 2 0 (333,667]
## 15 (1.18e+04,1.27e+04] iPad 1 1 (-0.99,333]
## 16 (1.18e+04,1.27e+04] iPad mini 0 (-0.99,333]
## 17 (1.09e+04,1.18e+04] Unknown 1 (-0.99,333]
## 18 (1e+04,1.09e+04] iPad Air 1 (-0.99,333]
## 19 (1.18e+04,1.27e+04] Unknown 0 (-0.99,333]
## 20 (1e+04,1.09e+04] iPad 1 0 (-0.99,333]
## 21 (1e+04,1.09e+04] iPad mini 0 (-0.99,333]
## 22 (1e+04,1.09e+04] iPad 4 1 (-0.99,333]
## 23 (1.09e+04,1.18e+04] iPad 2 1 (-0.99,333]
## 24 (1.09e+04,1.18e+04] iPad Air 0 (333,667]
## 25 (1.18e+04,1.27e+04] iPad 1 0 (-0.99,333]
## 26 (1e+04,1.09e+04] iPad 4 0 (-0.99,333]
## 27 (1e+04,1.09e+04] iPad 3 0 (-0.99,333]
## 28 (1.18e+04,1.27e+04] iPad Air 1 (-0.99,333]
## 29 (1.18e+04,1.27e+04] Unknown 1 (-0.99,333]
## 30 (1.09e+04,1.18e+04] iPad mini 3 0 (333,667]
## 31 (1.18e+04,1.27e+04] iPad mini 2 1 (-0.99,333]
## 32 (1.09e+04,1.18e+04] iPad 4 0 (-0.99,333]
## 33 (1.18e+04,1.27e+04] iPad 3 0 (-0.99,333]
## 34 (1.18e+04,1.27e+04] iPad 4 0 (-0.99,333]
## 35 (1e+04,1.09e+04] Unknown 1 (-0.99,333]
## 36 (1.09e+04,1.18e+04] iPad 3 0 (-0.99,333]
## 37 (1e+04,1.09e+04] iPad Air 2 0 (333,667]
## 38 (1.18e+04,1.27e+04] iPad 4 1 (-0.99,333]
## 39 (1.18e+04,1.27e+04] iPad Air 2 0 (333,667]
## 40 (1e+04,1.09e+04] Unknown 0 (-0.99,333]
## 41 (1e+04,1.09e+04] iPad mini 2 1 (-0.99,333]
## 42 (1.09e+04,1.18e+04] iPad Air 1 (-0.99,333]
## 43 (1e+04,1.09e+04] iPad Air 0 (333,667]
## 44 (1.18e+04,1.27e+04] iPad 3 1 (-0.99,333]
## 45 (1.18e+04,1.27e+04] iPad Air 0 (333,667]
## 46 (1.09e+04,1.18e+04] iPad 1 1 (-0.99,333]
## 47 (1.09e+04,1.18e+04] iPad mini 2 0 (-0.99,333]
## 48 (1.09e+04,1.18e+04] Unknown 0 (333,667]
## 49 (1.18e+04,1.27e+04] iPad mini 3 0 (333,667]
## 50 (1e+04,1.09e+04] iPad Air 2 1 (-0.99,333]
## 51 (1.09e+04,1.18e+04] iPad 4 1 (-0.99,333]
## 52 (1.09e+04,1.18e+04] iPad mini 2 0 (333,667]
## 53 (1.09e+04,1.18e+04] iPad 3 1 (-0.99,333]
## 54 (1.09e+04,1.18e+04] iPad Air 2 1 (333,667]
## 55 (1e+04,1.09e+04] iPad Air 0 (-0.99,333]
## 56 (1.09e+04,1.18e+04] iPad Air 0 (-0.99,333]
## 57 (1.18e+04,1.27e+04] iPad mini 2 0 (-0.99,333]
## 58 (1.09e+04,1.18e+04] iPad mini 2 1 (-0.99,333]
## 59 (1.09e+04,1.18e+04] iPad Air 2 1 (-0.99,333]
## 60 (1.09e+04,1.18e+04] iPad mini 0 (333,667]
## 61 (1.18e+04,1.27e+04] iPad Air 0 (-0.99,333]
## 62 (1.09e+04,1.18e+04] iPad 4 0 (333,667]
## 63 (1.09e+04,1.18e+04] iPad Air 2 0 (667,1e+03]
## 64 (1.18e+04,1.27e+04] iPad Air 2 1 (-0.99,333]
## 65 (1e+04,1.09e+04] iPad Air 2 1 (333,667]
## 66 (1e+04,1.09e+04] iPad mini 2 0 (-0.99,333]
## 67 (1.18e+04,1.27e+04] iPad 4 0 (333,667]
## 68 (1e+04,1.09e+04] iPad mini 3 0 (333,667]
## 69 (1e+04,1.09e+04] iPad mini 3 1 (-0.99,333]
## 70 (1.18e+04,1.27e+04] Unknown 0 (-0.99,333]
## 71 (1.18e+04,1.27e+04] iPad 1 0 (-0.99,333]
## 72 (1.18e+04,1.27e+04] iPad Air 2 1 (333,667]
## 73 (1.18e+04,1.27e+04] Unknown 0 (333,667]
## 74 (1.18e+04,1.27e+04] iPad mini 3 1 (-0.99,333]
## 75 (1.18e+04,1.27e+04] iPad Air 2 0 (667,1e+03]
## 76 (1.18e+04,1.27e+04] iPad 4 0 (-0.99,333]
## 77 (1.18e+04,1.27e+04] iPad Air 2 0 (333,667]
## 78 (1.09e+04,1.18e+04] iPad 2 0 (333,667]
## 79 (1e+04,1.09e+04] iPad mini 2 0 (333,667]
## 80 (1.09e+04,1.18e+04] iPad 3 0 (333,667]
## 81 (1.09e+04,1.18e+04] iPad mini 3 0 (-0.99,333]
## 82 (1.09e+04,1.18e+04] iPad mini 3 1 (-0.99,333]
## 83 (1.09e+04,1.18e+04] iPad mini 3 1 (333,667]
## 84 (1.18e+04,1.27e+04] iPad 2 0 (-0.99,333]
## 85 (1.18e+04,1.27e+04] iPad mini 0 (-0.99,333]
## 86 (1.18e+04,1.27e+04] iPad mini 2 0 (333,667]
## 87 (1e+04,1.09e+04] iPad Air 1 (333,667]
## 88 (1e+04,1.09e+04] iPad mini 0 (333,667]
## 89 (1.18e+04,1.27e+04] Unknown 1 (333,667]
## 90 (1.18e+04,1.27e+04] iPad 3 0 (-0.99,333]
## 91 (1.18e+04,1.27e+04] iPad Air 0 (-0.99,333]
## 92 (1e+04,1.09e+04] iPad 4 0 (333,667]
## 93 (1e+04,1.09e+04] iPad mini 3 0 (-0.99,333]
## 94 (1.09e+04,1.18e+04] Unknown 1 (333,667]
## 95 (1.09e+04,1.18e+04] iPad Air 0 (667,1e+03]
## 96 (1.18e+04,1.27e+04] iPad mini 0 (333,667]
## 97 (1.18e+04,1.27e+04] iPad mini 3 0 (-0.99,333]
## 98 (1.09e+04,1.18e+04] iPad Air 2 0 (-0.99,333]
## 99 (1.09e+04,1.18e+04] iPad mini 2 1 (333,667]
## 100 (1.18e+04,1.27e+04] iPad 1 1 (-0.99,333]
## 101 (1.18e+04,1.27e+04] iPad 2 0 (333,667]
## 102 (1.18e+04,1.27e+04] iPad mini 1 (-0.99,333]
## 103 (1.18e+04,1.27e+04] iPad mini 2 0 (-0.99,333]
## 104 (1.18e+04,1.27e+04] iPad mini 2 1 (333,667]
## 105 (1.18e+04,1.27e+04] iPad mini 3 0 (333,667]
## 106 (1.18e+04,1.27e+04] iPad mini 3 1 (333,667]
## 107 (1e+04,1.09e+04] iPad mini 3 1 (333,667]
## 108 (1.09e+04,1.18e+04] iPad 4 1 (333,667]
## 109 (1.09e+04,1.18e+04] iPad mini 0 (667,1e+03]
## 110 (1.09e+04,1.18e+04] iPad mini 1 (333,667]
## 111 (1.09e+04,1.18e+04] iPad mini 3 0 (667,1e+03]
## 112 (1.09e+04,1.18e+04] iPad mini 3 1 (667,1e+03]
## 113 (1.09e+04,1.18e+04] iPad mini Retina 0 (-0.99,333]
## 114 (1.09e+04,1.18e+04] iPad mini Retina 0 (333,667]
## 115 (1.18e+04,1.27e+04] Unknown 0 (333,667]
## 116 (1.18e+04,1.27e+04] Unknown 0 (667,1e+03]
## 117 (1.18e+04,1.27e+04] Unknown 1 (-0.99,333]
## 118 (1.18e+04,1.27e+04] iPad 2 1 (-0.99,333]
## 119 (1.18e+04,1.27e+04] iPad 3 0 (333,667]
## 120 (1.18e+04,1.27e+04] iPad 4 1 (-0.99,333]
## 121 (1.18e+04,1.27e+04] iPad Air 0 (333,667]
## 122 (1.18e+04,1.27e+04] iPad Air 0 (667,1e+03]
## 123 (1.18e+04,1.27e+04] iPad Air 1 (333,667]
## 124 (1.18e+04,1.27e+04] iPad Air 2 1 (667,1e+03]
## 125 (1e+04,1.09e+04] Unknown 0 (333,667]
## 126 (1e+04,1.09e+04] iPad 4 1 (333,667]
## 127 (1e+04,1.09e+04] iPad Air 2 0 (-0.99,333]
## 128 (1e+04,1.09e+04] iPad mini 2 1 (333,667]
## 129 (1e+04,1.09e+04] iPad mini Retina 0 (-0.99,333]
## 130 (1.09e+04,1.18e+04] Unknown 0 (667,1e+03]
## 131 (1.09e+04,1.18e+04] Unknown 1 (667,1e+03]
## 132 (1.09e+04,1.18e+04] iPad 1 1 (333,667]
## 133 (1.09e+04,1.18e+04] iPad 4 0 (667,1e+03]
## 134 (1.09e+04,1.18e+04] iPad 5 1 (-0.99,333]
## 135 (1.09e+04,1.18e+04] iPad Air 1 (333,667]
## 136 (1.09e+04,1.18e+04] iPad Air 2 1 (667,1e+03]
## 137 (1.18e+04,1.27e+04] iPad 2 0 (667,1e+03]
## 138 (1.18e+04,1.27e+04] iPad 3 1 (-0.99,333]
## 139 (1.18e+04,1.27e+04] iPad Air 2 0 (-0.99,333]
## 140 (1.18e+04,1.27e+04] iPad Air 2 1 (333,667]
## 141 (1.18e+04,1.27e+04] iPad mini 0 (333,667]
## 142 (1.18e+04,1.27e+04] iPad mini 3 0 (-0.99,333]
## 143 (1.18e+04,1.27e+04] iPad mini 3 0 (667,1e+03]
## 144 (1.18e+04,1.27e+04] iPad mini 3 1 (-0.99,333]
## 145 (1.18e+04,1.27e+04] iPad mini 3 1 (667,1e+03]
## 146 (1.18e+04,1.27e+04] iPad mini Retina 0 (333,667]
## 147 (1e+04,1.09e+04] iPad 2 0 (333,667]
## 148 (1e+04,1.09e+04] iPad 3 0 (333,667]
## 149 (1e+04,1.09e+04] iPad 3 0 (667,1e+03]
## 150 (1e+04,1.09e+04] iPad 3 1 (333,667]
## 151 (1e+04,1.09e+04] iPad Air 2 0 (667,1e+03]
## 152 (1e+04,1.09e+04] iPad Air 2 1 (667,1e+03]
## 153 (1e+04,1.09e+04] iPad mini Retina 1 (-0.99,333]
## .src .n
## 1 Train 100
## 2 Train 93
## 3 Test 80
## 4 Train 77
## 5 Train 74
## 6 Train 70
## 7 Test 70
## 8 Train 59
## 9 Test 59
## 10 Train 58
## 11 Train 57
## 12 Train 56
## 13 Train 55
## 14 Train 50
## 15 Test 50
## 16 Test 48
## 17 Train 47
## 18 Train 43
## 19 Test 42
## 20 Train 42
## 21 Train 42
## 22 Train 40
## 23 Train 38
## 24 Train 38
## 25 Test 38
## 26 Train 36
## 27 Train 35
## 28 Test 34
## 29 Test 33
## 30 Train 31
## 31 Test 31
## 32 Train 30
## 33 Test 30
## 34 Test 30
## 35 Train 30
## 36 Train 28
## 37 Train 28
## 38 Test 26
## 39 Test 26
## 40 Train 26
## 41 Train 26
## 42 Train 24
## 43 Train 24
## 44 Test 23
## 45 Test 22
## 46 Train 21
## 47 Train 20
## 48 Train 19
## 49 Test 19
## 50 Train 19
## 51 Train 18
## 52 Train 18
## 53 Train 17
## 54 Train 17
## 55 Train 17
## 56 Train 16
## 57 Test 16
## 58 Train 15
## 59 Train 14
## 60 Train 14
## 61 Test 14
## 62 Train 13
## 63 Train 13
## 64 Test 13
## 65 Train 13
## 66 Train 13
## 67 Test 12
## 68 Train 12
## 69 Train 12
## 70 Train 11
## 71 Train 11
## 72 Test 11
## 73 Test 10
## 74 Test 10
## 75 Test 9
## 76 Train 8
## 77 Train 8
## 78 Train 7
## 79 Train 7
## 80 Train 6
## 81 Train 6
## 82 Train 6
## 83 Train 6
## 84 Train 6
## 85 Train 6
## 86 Test 6
## 87 Train 6
## 88 Train 6
## 89 Test 5
## 90 Train 5
## 91 Train 5
## 92 Train 5
## 93 Train 5
## 94 Train 4
## 95 Train 4
## 96 Test 4
## 97 Test 4
## 98 Train 3
## 99 Train 3
## 100 Train 3
## 101 Test 3
## 102 Train 3
## 103 Train 3
## 104 Test 3
## 105 Train 3
## 106 Test 3
## 107 Train 3
## 108 Train 2
## 109 Train 2
## 110 Train 2
## 111 Train 2
## 112 Train 2
## 113 Train 2
## 114 Train 2
## 115 Train 2
## 116 Test 2
## 117 Train 2
## 118 Train 2
## 119 Test 2
## 120 Train 2
## 121 Train 2
## 122 Test 2
## 123 Test 2
## 124 Test 2
## 125 Train 2
## 126 Train 2
## 127 Train 2
## 128 Train 2
## 129 Train 2
## 130 Train 1
## 131 Train 1
## 132 Train 1
## 133 Train 1
## 134 Train 1
## 135 Train 1
## 136 Train 1
## 137 Test 1
## 138 Train 1
## 139 Test 1
## 140 Train 1
## 141 Train 1
## 142 Train 1
## 143 Test 1
## 144 Train 1
## 145 Test 1
## 146 Train 1
## 147 Train 1
## 148 Train 1
## 149 Train 1
## 150 Train 1
## 151 Train 1
## 152 Train 1
## 153 Train 1
## UniqueID.cut.fctr .src .n
## 1 (1e+04,1.09e+04] Train 887
## 2 (1.09e+04,1.18e+04] Train 886
## 3 (1.18e+04,1.27e+04] Test 798
## 4 (1.18e+04,1.27e+04] Train 88
## productline .src .n
## 1 iPad 2 Train 286
## 2 iPad mini Train 277
## 3 iPad 1 Train 227
## 4 Unknown Train 204
## 5 iPad Air Train 180
## 6 iPad Air 2 Train 171
## 7 iPad 4 Train 157
## 8 iPad 2 Test 154
## 9 iPad 3 Train 153
## 10 iPad mini Test 111
## 11 iPad mini 2 Train 107
## 12 Unknown Test 92
## 13 iPad mini 3 Train 90
## 14 iPad 1 Test 88
## 15 iPad Air Test 74
## 16 iPad 4 Test 68
## 17 iPad Air 2 Test 62
## 18 iPad mini 2 Test 56
## 19 iPad 3 Test 55
## 20 iPad mini 3 Test 38
## 21 iPad mini Retina Train 8
## 22 iPad 5 Train 1
## biddable .src .n
## 1 0 Train 1024
## 2 1 Train 837
## 3 0 Test 422
## 4 1 Test 376
## startprice.cut.fctr .src .n
## 1 (-0.99,333] Train 1466
## 2 (-0.99,333] Test 652
## 3 (333,667] Train 365
## 4 (333,667] Test 128
## 5 (667,1e+03] Train 30
## 6 (667,1e+03] Test 18
## .src .n
## 1 Train 1861
## 2 Test 798if (!is.null(glb_obs_drop_condition)) {
print(sprintf("Running glb_obs_drop_condition filter: %s", glb_obs_drop_condition))
glb_allobs_df <- do.call("subset",
list(glb_allobs_df, parse(text=paste0("!(", glb_obs_drop_condition, ")"))))
dsp_partition_stats(obs_df=glb_allobs_df, vars=myget_symbols(glb_obs_drop_condition))
}## [1] "Running glb_obs_drop_condition filter: (UniqueID %in% c(NULL\n , 11234 #sold=0; 2 other dups(10306, 11503) are sold=1\n , 11844 #sold=0; 3 other dups(11721, 11738, 11812) are sold=1\n )) | (productline %in% c('iPad 5', 'iPad mini Retina')) |\n (biddable != 1)"
## [1] "Partition stats:"
## UniqueID.cut.fctr productline biddable startprice.cut.fctr .src .n
## 1 (1e+04,1.09e+04] iPad 2 1 (-0.87,293] Train 100
## 2 (1e+04,1.09e+04] iPad 1 1 (-0.87,293] Train 93
## 3 (1e+04,1.09e+04] iPad mini 1 (-0.87,293] Train 74
## 4 (1.18e+04,1.27e+04] iPad 2 1 (-0.87,293] Test 70
## 5 (1.18e+04,1.27e+04] iPad mini 1 (-0.87,293] Test 59
## 6 (1e+04,1.09e+04] iPad 3 1 (-0.87,293] Train 58
## 7 (1.09e+04,1.18e+04] iPad mini 1 (-0.87,293] Train 53
## 8 (1.18e+04,1.27e+04] iPad 1 1 (-0.87,293] Test 50
## 9 (1.09e+04,1.18e+04] Unknown 1 (-0.87,293] Train 45
## 10 (1e+04,1.09e+04] iPad 4 1 (-0.87,293] Train 39
## 11 (1e+04,1.09e+04] iPad Air 1 (-0.87,293] Train 38
## 12 (1.09e+04,1.18e+04] iPad 2 1 (-0.87,293] Train 37
## 13 (1.18e+04,1.27e+04] Unknown 1 (-0.87,293] Test 32
## 14 (1.18e+04,1.27e+04] iPad Air 1 (-0.87,293] Test 30
## 15 (1e+04,1.09e+04] Unknown 1 (-0.87,293] Train 29
## 16 (1.18e+04,1.27e+04] iPad mini 2 1 (-0.87,293] Test 27
## 17 (1.18e+04,1.27e+04] iPad 4 1 (-0.87,293] Test 25
## 18 (1e+04,1.09e+04] iPad mini 2 1 (-0.87,293] Train 25
## 19 (1.09e+04,1.18e+04] iPad Air 1 (-0.87,293] Train 23
## 20 (1.18e+04,1.27e+04] iPad 3 1 (-0.87,293] Test 22
## 21 (1.09e+04,1.18e+04] iPad 1 1 (-0.87,293] Train 21
## 22 (1e+04,1.09e+04] iPad Air 2 1 (293,587] Train 19
## 23 (1.09e+04,1.18e+04] iPad 4 1 (-0.87,293] Train 18
## 24 (1.09e+04,1.18e+04] iPad Air 2 1 (293,587] Train 16
## 25 (1.09e+04,1.18e+04] iPad 3 1 (-0.87,293] Train 15
## 26 (1.09e+04,1.18e+04] iPad mini 2 1 (-0.87,293] Train 14
## 27 (1.09e+04,1.18e+04] iPad Air 2 1 (-0.87,293] Train 13
## 28 (1e+04,1.09e+04] iPad Air 2 1 (-0.87,293] Train 13
## 29 (1.18e+04,1.27e+04] iPad Air 2 1 (-0.87,293] Test 11
## 30 (1.18e+04,1.27e+04] iPad Air 2 1 (293,587] Test 11
## 31 (1e+04,1.09e+04] iPad Air 1 (293,587] Train 11
## 32 (1.18e+04,1.27e+04] iPad mini 3 1 (-0.87,293] Test 10
## 33 (1e+04,1.09e+04] iPad mini 3 1 (-0.87,293] Train 8
## 34 (1e+04,1.09e+04] iPad mini 3 1 (293,587] Train 7
## 35 (1.09e+04,1.18e+04] iPad mini 3 1 (293,587] Train 6
## 36 (1.18e+04,1.27e+04] iPad Air 1 (293,587] Test 6
## 37 (1.18e+04,1.27e+04] iPad mini 2 1 (293,587] Test 6
## 38 (1.09e+04,1.18e+04] Unknown 1 (293,587] Train 5
## 39 (1.09e+04,1.18e+04] iPad mini 1 (293,587] Train 5
## 40 (1.09e+04,1.18e+04] iPad mini 3 1 (-0.87,293] Train 5
## 41 (1.18e+04,1.27e+04] Unknown 1 (293,587] Test 5
## 42 (1.09e+04,1.18e+04] iPad mini 2 1 (293,587] Train 4
## 43 (1.18e+04,1.27e+04] iPad Air 2 1 (587,881] Test 4
## 44 (1.18e+04,1.27e+04] iPad mini 1 (-0.87,293] Train 4
## 45 (1.09e+04,1.18e+04] iPad Air 2 1 (587,881] Train 3
## 46 (1.09e+04,1.18e+04] iPad mini 3 1 (587,881] Train 3
## 47 (1.18e+04,1.27e+04] iPad 1 1 (-0.87,293] Train 3
## 48 (1.18e+04,1.27e+04] iPad mini 3 1 (587,881] Test 3
## 49 (1e+04,1.09e+04] iPad 4 1 (293,587] Train 3
## 50 (1e+04,1.09e+04] iPad mini 2 1 (293,587] Train 3
## 51 (1.09e+04,1.18e+04] Unknown 1 (587,881] Train 2
## 52 (1.09e+04,1.18e+04] iPad 3 1 (293,587] Train 2
## 53 (1.09e+04,1.18e+04] iPad 4 1 (293,587] Train 2
## 54 (1.09e+04,1.18e+04] iPad Air 1 (293,587] Train 2
## 55 (1.18e+04,1.27e+04] Unknown 1 (-0.87,293] Train 2
## 56 (1.18e+04,1.27e+04] iPad 2 1 (-0.87,293] Train 2
## 57 (1.18e+04,1.27e+04] iPad 4 1 (-0.87,293] Train 2
## 58 (1.09e+04,1.18e+04] iPad 1 1 (293,587] Train 1
## 59 (1.09e+04,1.18e+04] iPad 2 1 (293,587] Train 1
## 60 (1.18e+04,1.27e+04] Unknown 1 (587,881] Test 1
## 61 (1.18e+04,1.27e+04] iPad 3 1 (-0.87,293] Train 1
## 62 (1.18e+04,1.27e+04] iPad 3 1 (293,587] Test 1
## 63 (1.18e+04,1.27e+04] iPad 4 1 (293,587] Test 1
## 64 (1.18e+04,1.27e+04] iPad Air 2 1 (293,587] Train 1
## 65 (1.18e+04,1.27e+04] iPad mini 2 1 (587,881] Test 1
## 66 (1.18e+04,1.27e+04] iPad mini 3 1 (293,587] Test 1
## 67 (1.18e+04,1.27e+04] iPad mini 3 1 (293,587] Train 1
## 68 (1e+04,1.09e+04] Unknown 1 (293,587] Train 1
## 69 (1e+04,1.09e+04] iPad 3 1 (293,587] Train 1
## 70 (1e+04,1.09e+04] iPad Air 2 1 (587,881] Train 1
## UniqueID.cut.fctr .src .n
## 1 (1e+04,1.09e+04] Train 523
## 2 (1.18e+04,1.27e+04] Test 376
## 3 (1.09e+04,1.18e+04] Train 296
## 4 (1.18e+04,1.27e+04] Train 16
## productline .src .n
## 1 iPad 2 Train 140
## 2 iPad mini Train 136
## 3 iPad 1 Train 118
## 4 Unknown Train 84
## 5 iPad 3 Train 77
## 6 iPad Air Train 74
## 7 iPad 2 Test 70
## 8 iPad Air 2 Train 66
## 9 iPad 4 Train 64
## 10 iPad mini Test 59
## 11 iPad 1 Test 50
## 12 iPad mini 2 Train 46
## 13 Unknown Test 38
## 14 iPad Air Test 36
## 15 iPad mini 2 Test 34
## 16 iPad mini 3 Train 30
## 17 iPad 4 Test 26
## 18 iPad Air 2 Test 26
## 19 iPad 3 Test 23
## 20 iPad mini 3 Test 14
## biddable .src .n
## 1 1 Train 835
## 2 1 Test 376
## startprice.cut.fctr .src .n
## 1 (-0.87,293] Train 735
## 2 (-0.87,293] Test 336
## 3 (293,587] Train 91
## 4 (293,587] Test 31
## 5 (587,881] Test 9
## 6 (587,881] Train 9
## .src .n
## 1 Train 835
## 2 Test 376# Check for duplicates by all features
require(gdata)## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
##
## The following object is masked from 'package:stats':
##
## nobs
##
## The following object is masked from 'package:utils':
##
## object.size#print(names(glb_allobs_df))
dup_allobs_df <- glb_allobs_df[duplicated2(subset(glb_allobs_df,
select=-c(UniqueID, sold, .src))), ]
dup_allobs_df <- orderBy(~productline+description+startprice+biddable, dup_allobs_df)
print(sprintf("Found %d duplicates by all features:", nrow(dup_allobs_df)))## [1] "Found 165 duplicates by all features:"myprint_df(dup_allobs_df)## description biddable startprice condition cellular
## 1711 1 0.99 For parts or not working Unknown
## 2608 1 0.99 For parts or not working Unknown
## 293 1 5.00 Used Unknown
## 478 1 5.00 Used Unknown
## 1351 1 150.00 Used Unknown
## 1386 1 150.00 Used Unknown
## carrier color storage productline sold UniqueID .src
## 1711 Unknown Unknown 16 Unknown 1 11711 Train
## 2608 Unknown Unknown 16 Unknown NA 12608 Test
## 293 Unknown White 16 Unknown 1 10293 Train
## 478 Unknown White 16 Unknown 1 10478 Train
## 1351 Unknown Unknown Unknown Unknown 1 11351 Train
## 1386 Unknown Unknown Unknown Unknown 0 11386 Train
## description
## 233
## 487
## 1646 PLEASE READ THE ENTIRE DESCRIPTION! THE ANSWER TO YOUR QUESTION(S) ARE USUALLY ALREADY IN THE
## 442
## 2214
## 1072 Definite must-have. This Apple iPad Mini is pre-owned and has some signs of use, there is a nick/
## biddable startprice condition cellular carrier color
## 233 1 0.99 Used 0 None Unknown
## 487 1 80.00 Used 0 None Black
## 1646 1 40.00 For parts or not working 0 None Unknown
## 442 1 9.99 Used 0 None White
## 2214 1 150.00 Used 0 None Unknown
## 1072 1 199.00 Used 1 AT&T Unknown
## storage productline sold UniqueID .src
## 233 16 iPad 2 1 10233 Train
## 487 16 iPad 2 1 10487 Train
## 1646 64 iPad 2 1 11646 Train
## 442 16 iPad 3 1 10442 Train
## 2214 16 iPad mini NA 12214 Test
## 1072 64 iPad mini 0 11072 Train
## description
## 1223 Good condition and functional 32GB iPad Mini Wifi - Black. Please read full discription
## 2388 Good condition and functional 32GB iPad Mini Wifi - Black. Please read full discription
## 524
## 2111
## 510
## 2126
## biddable startprice condition cellular carrier color storage
## 1223 1 179 Used 0 None Black 32
## 2388 1 179 Used 0 None Black 32
## 524 1 200 Used 0 None Space Gray 64
## 2111 1 200 Used 0 None Space Gray 64
## 510 1 99 Used 0 None Space Gray 16
## 2126 1 99 Used 0 None Space Gray 16
## productline sold UniqueID .src
## 1223 iPad mini 0 11223 Train
## 2388 iPad mini NA 12388 Test
## 524 iPad mini 2 1 10524 Train
## 2111 iPad mini 2 NA 12111 Test
## 510 iPad mini 3 1 10510 Train
## 2126 iPad mini 3 NA 12126 Test# print(dup_allobs_df[, c(glb_id_var, glb_rsp_var_raw,
# "description", "startprice", "biddable")])
# write.csv(dup_allobs_df[, c("UniqueID"), FALSE], "ebayipads_dups.csv", row.names=FALSE)
dupobs_df <- tidyr::unite(dup_allobs_df, "allfeats", -c(sold, UniqueID, .src), sep="#")
# dupobs_df <- dplyr::group_by(dupobs_df, allfeats)
# dupobs_df <- dupobs_df[, "UniqueID", FALSE]
# dupobs_df <- ungroup(dupobs_df)
#
# dupobs_df$.rownames <- row.names(dupobs_df)
grpobs_df <- data.frame(allfeats=unique(dupobs_df[, "allfeats"]))
grpobs_df$.grpid <- row.names(grpobs_df)
dupobs_df <- merge(dupobs_df, grpobs_df)
# dupobs_tbl <- table(dupobs_df$.grpid)
# print(max(dupobs_tbl))
# print(dupobs_tbl[which.max(dupobs_tbl)])
# print(dupobs_df[dupobs_df$.grpid == names(dupobs_tbl[which.max(dupobs_tbl)]), ])
# print(dupobs_df[dupobs_df$.grpid == 106, ])
# for (grpid in c(9, 17, 31, 36, 53))
# print(dupobs_df[dupobs_df$.grpid == grpid, ])
dupgrps_df <- as.data.frame(table(dupobs_df$.grpid, dupobs_df$sold, useNA="ifany"))
names(dupgrps_df)[c(1,2)] <- c(".grpid", "sold")
dupgrps_df$.grpid <- as.numeric(as.character(dupgrps_df$.grpid))
dupgrps_df <- tidyr::spread(dupgrps_df, sold, Freq)
names(dupgrps_df)[-1] <- paste("sold", names(dupgrps_df)[-1], sep=".")
dupgrps_df$.freq <- sapply(1:nrow(dupgrps_df), function(row) sum(dupgrps_df[row, -1]))
myprint_df(orderBy(~-.freq, dupgrps_df))## .grpid sold.0 sold.1 sold.NA .freq
## 23 23 0 6 3 9
## 56 56 0 4 1 5
## 4 4 0 1 3 4
## 10 10 0 3 1 4
## 20 20 0 3 1 4
## 11 11 0 2 1 3
## .grpid sold.0 sold.1 sold.NA .freq
## 49 49 0 2 1 3
## 16 16 0 0 2 2
## 25 25 0 1 1 2
## 31 31 0 0 2 2
## 44 44 0 2 0 2
## 48 48 2 0 0 2
## .grpid sold.0 sold.1 sold.NA .freq
## 63 63 0 1 1 2
## 65 65 0 1 1 2
## 66 66 2 0 0 2
## 67 67 1 0 1 2
## 68 68 0 1 1 2
## 69 69 0 1 1 2print("sold Conflicts:")## [1] "sold Conflicts:"print(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 3 3 1 1 0 2
## 59 59 1 1 0 2
## 60 60 1 1 0 2#dupobs_df[dupobs_df$.grpid == 4, ]
if (nrow(subset(dupgrps_df, (sold.0 > 0) & (sold.1 > 0) & (sold.0 != sold.1))) > 0)
stop("Duplicate conflicts are resolvable")
#mydsp_obs(list(productline.contains="iPad 1", storage.contains="16", color.contains="Black", carrier.contains="None", cellular.contains="0", condition.contains="Used", startprice=80), cols=c("productline", "storage", "color", "carrier", "cellular", "condition", "startprice", "sold"))
print("Test & Train Groups:")## [1] "Test & Train Groups:"print(subset(dupgrps_df, (sold.NA > 0)))## .grpid sold.0 sold.1 sold.NA .freq
## 1 1 0 1 1 2
## 4 4 0 1 3 4
## 7 7 0 1 1 2
## 8 8 0 0 2 2
## 10 10 0 3 1 4
## 11 11 0 2 1 3
## 12 12 0 2 1 3
## 15 15 0 2 1 3
## 16 16 0 0 2 2
## 17 17 0 1 1 2
## 19 19 0 2 1 3
## 20 20 0 3 1 4
## 21 21 0 1 1 2
## 23 23 0 6 3 9
## 24 24 0 0 2 2
## 25 25 0 1 1 2
## 26 26 0 1 1 2
## 27 27 0 2 1 3
## 30 30 0 1 1 2
## 31 31 0 0 2 2
## 32 32 0 1 2 3
## 34 34 0 1 1 2
## 36 36 1 0 2 3
## 37 37 1 0 1 2
## 38 38 1 0 1 2
## 41 41 0 1 1 2
## 47 47 0 1 1 2
## 49 49 0 2 1 3
## 52 52 0 1 1 2
## 54 54 0 1 1 2
## 56 56 0 4 1 5
## 57 57 0 1 1 2
## 58 58 0 1 1 2
## 61 61 1 0 1 2
## 63 63 0 1 1 2
## 64 64 0 2 1 3
## 65 65 0 1 1 2
## 67 67 1 0 1 2
## 68 68 0 1 1 2
## 69 69 0 1 1 2glb_allobs_df <- merge(glb_allobs_df, dupobs_df[, c(glb_id_var, ".grpid")],
by=glb_id_var, all.x=TRUE)
glb_exclude_vars_as_features <- c(".grpid", glb_exclude_vars_as_features)
# !_sp
# spd_allobs_df <- read.csv(paste0(glb_out_pfx, "sp_predict.csv"))
# if (nrow(spd_allobs_df) != nrow(glb_allobs_df))
# stop("mismatches between spd_allobs_df & glb_allobs_df")
# mrg_allobs_df <- merge(glb_allobs_df, spd_allobs_df)
# if (nrow(mrg_allobs_df) != nrow(glb_allobs_df))
# stop("mismatches between mrg_allobs_df & glb_allobs_df")
# mrg_allobs_df$startprice.diff <- mrg_allobs_df$startprice -
# (exp(mrg_allobs_df$startprice.log.predict.) - 1)
# mrg_allobs_df$startprice.log.diff <- log(1 + mrg_allobs_df$startprice) -
# mrg_allobs_df$startprice.log.predict.
# print(myplot_scatter(mrg_allobs_df, "startprice", "startprice.diff",
# colorcol_name = "biddable"))
# print(myplot_scatter(mrg_allobs_df, "startprice", "startprice.log.diff",
# colorcol_name = "biddable"))
# print(myplot_histogram(mrg_allobs_df, "startprice.diff",
# fill_col_name = "biddable"))
# print(myplot_histogram(mrg_allobs_df, "startprice.log.diff",
# fill_col_name = "biddable"))
# glb_allobs_df <- mrg_allobs_df
# glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
# "startprice.diff", "startprice.log", "startprice.log.predict.")
###
#stop(here"); glb_to_sav(); all.equal(sav_allobs_df, glb_allobs_df); glb_allobs_df <- sav_allobs_df
dsp_partition_stats(obs_df=glb_allobs_df,
vars=myget_symbols(glb_obs_repartition_train_condition))## [1] "Partition stats:"
## sold startprice.cut.fctr .src .n
## 1 1 (-0.87,293] Train 594
## 2 NA (-0.87,293] Test 336
## 3 0 (-0.87,293] Train 141
## 4 0 (293,587] Train 48
## 5 1 (293,587] Train 43
## 6 NA (293,587] Test 31
## 7 NA (587,881] Test 9
## 8 0 (587,881] Train 8
## 9 1 (587,881] Train 1
## sold .src .n
## 1 1 Train 638
## 2 NA Test 376
## 3 0 Train 197
## startprice.cut.fctr .src .n
## 1 (-0.87,293] Train 735
## 2 (-0.87,293] Test 336
## 3 (293,587] Train 91
## 4 (293,587] Test 31
## 5 (587,881] Test 9
## 6 (587,881] Train 9
## .src .n
## 1 Train 835
## 2 Test 376if (!is.null(glb_obs_repartition_train_condition)) {
print(sprintf("Running glb_obs_repartition_train_condition filter: %s",
glb_obs_repartition_train_condition))
# glb_allobs_df <- mutate(glb_allobs_df, .src=ifelse(!is.na(sold) & (sold == 1),
# "Train", "Test"))
# glb_allobs_df <- mutate_(glb_allobs_df,
# .src=interp(ifelse(eval(parse(text="!is.na(sold) & (sold == 1)")),
# "Train", "Test")))
# glb_allobs_df <- within(glb_allobs_df, {
# .src <- ifelse(eval(parse(text="!is.na(sold) & (sold == 1)")),
# "Train", "Test")
# })
# glb_allobs_df <- within(glb_allobs_df, {
# if(eval(parse(text="!is.na(sold) & (sold == 1)"))) .src <- "Train" else
# .src <- "Test"
# })
# with(glb_allobs_df, {
# src <- ifelse(eval(parse(text="!is.na(sold) & (sold == 1)")),
# "Train", "Test")
# })
# glb_allobs_df$.src <- sapply(1:nrow(glb_allobs_df), function (row_ix) ifelse)
# glb_allobs_df[parse(text=paste0("!(", glb_obs_drop_condition, ")")), ".src"] <- do.call("subset",
# list(glb_allobs_df, ))
glb_trnobs_df <- do.call("subset", list(glb_allobs_df,
parse(text=paste0(" (", glb_obs_repartition_train_condition, ")"))))
glb_trnobs_df$.src <- "Train"
glb_newobs_df <- do.call("subset", list(glb_allobs_df,
parse(text=paste0("!(", glb_obs_repartition_train_condition, ")"))))
glb_newobs_df$.src <- "Test"
glb_allobs_df <- rbind(glb_trnobs_df, glb_newobs_df)
dsp_partition_stats(obs_df=glb_allobs_df,
vars=myget_symbols(glb_obs_repartition_train_condition))
}## [1] "Running glb_obs_repartition_train_condition filter: !is.na(sold) & (sold == 1)"
## [1] "Partition stats:"
## sold startprice.cut.fctr .src .n
## 1 1 (-0.87,293] Train 594
## 2 NA (-0.87,293] Test 336
## 3 0 (-0.87,293] Test 141
## 4 0 (293,587] Test 48
## 5 1 (293,587] Train 43
## 6 NA (293,587] Test 31
## 7 NA (587,881] Test 9
## 8 0 (587,881] Test 8
## 9 1 (587,881] Train 1
## sold .src .n
## 1 1 Train 638
## 2 NA Test 376
## 3 0 Test 197
## startprice.cut.fctr .src .n
## 1 (-0.87,293] Train 594
## 2 (-0.87,293] Test 477
## 3 (293,587] Test 79
## 4 (293,587] Train 43
## 5 (587,881] Test 17
## 6 (587,881] Train 1
## .src .n
## 1 Train 638
## 2 Test 573# Only for _sp
# print(table(glb_allobs_df$sold, glb_allobs_df$.src, useNA = "ifany"))
# print(table(glb_allobs_df$sold, glb_allobs_df$biddable, glb_allobs_df$.src,
# useNA = "ifany"))
# glb_allobs_df$.src <- "Test"
# glb_allobs_df[!is.na(glb_allobs_df$sold) & (glb_allobs_df$sold == 1), ".src"] <- "Train"
# print(table(glb_allobs_df$sold, glb_allobs_df$.src, useNA = "ifany"))
# print(table(glb_allobs_df$sold, glb_allobs_df$biddable, glb_allobs_df$.src,
# useNA = "ifany"))
###
glb_chunks_df <- myadd_chunk(glb_chunks_df, "inspect.data", major.inc=TRUE)## label step_major step_minor label_minor bgn end elapsed
## 1 import.data 1 0 0 9.07 20.63 11.56
## 2 inspect.data 2 0 0 20.63 NA NAStep 2.0: inspect data
#print(str(glb_allobs_df))
#View(glb_allobs_df)
dsp_class_dstrb <- function(var) {
xtab_df <- mycreate_xtab_df(glb_allobs_df, c(".src", var))
rownames(xtab_df) <- xtab_df$.src
xtab_df <- subset(xtab_df, select=-.src)
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Performed repeatedly in other chunks
glb_chk_data <- function() {
# Histogram of predictor in glb_trnobs_df & glb_newobs_df
print(myplot_histogram(glb_allobs_df, glb_rsp_var_raw) + facet_wrap(~ .src))
if (glb_is_classification)
dsp_class_dstrb(var=ifelse(glb_rsp_var %in% names(glb_allobs_df),
glb_rsp_var, glb_rsp_var_raw))
mycheck_problem_data(glb_allobs_df)
}
glb_chk_data()## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## [1] "numeric data missing in : "
## sold
## 376
## [1] "numeric data w/ 0s in : "
## sold
## 197
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 753 0 0 0 0 0
## productline .grpid
## 0 NA# Create new features that help diagnostics
if (!is.null(glb_map_rsp_raw_to_var)) {
glb_allobs_df[, glb_rsp_var] <-
glb_map_rsp_raw_to_var(glb_allobs_df[, glb_rsp_var_raw])
mycheck_map_results(mapd_df=glb_allobs_df,
from_col_name=glb_rsp_var_raw, to_col_name=glb_rsp_var)
if (glb_is_classification) dsp_class_dstrb(glb_rsp_var)
}## startprice startprice.log10 .n
## 1 0.99 -0.004364805 201
## 2 100.00 2.000000000 52
## 3 50.00 1.698970004 51
## 4 150.00 2.176091259 41
## 5 1.00 0.000000000 31
## 6 99.99 1.999956568 29
## startprice startprice.log10 .n
## 2 100.00 2.000000 52
## 47 179.99 2.255248 5
## 89 14.99 1.175802 2
## 92 49.95 1.698535 2
## 169 111.00 2.045323 1
## 212 244.96 2.389095 1
## startprice startprice.log10 .n
## 244 528.00 2.722634 1
## 245 575.00 2.759668 1
## 246 600.00 2.778151 1
## 247 700.00 2.845098 1
## 248 800.00 2.903090 1
## 249 879.99 2.944478 1
# check distribution of all numeric data
dsp_numeric_feats_dstrb <- function(feats_vctr) {
for (feat in feats_vctr) {
print(sprintf("feat: %s", feat))
if (glb_is_regression)
gp <- myplot_scatter(df=glb_allobs_df, ycol_name=glb_rsp_var, xcol_name=feat,
smooth=TRUE)
if (glb_is_classification)
gp <- myplot_box(df=glb_allobs_df, ycol_names=feat, xcol_name=glb_rsp_var)
if (inherits(glb_allobs_df[, feat], "factor"))
gp <- gp + facet_wrap(reformulate(feat))
print(gp)
}
}
# dsp_numeric_vars_dstrb(setdiff(names(glb_allobs_df),
# union(myfind_chr_cols_df(glb_allobs_df),
# c(glb_rsp_var_raw, glb_rsp_var))))
add_new_diag_feats <- function(obs_df, ref_df=glb_allobs_df) {
require(plyr)
set.seed(169)
obs_df <- mutate(obs_df,
# <col_name>.NA=is.na(<col_name>),
# <col_name>.fctr=factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# <col_name>.fctr=relevel(factor(<col_name>,
# as.factor(union(obs_df$<col_name>, obs_twin_df$<col_name>))),
# "<ref_val>"),
# <col2_name>.fctr=relevel(factor(ifelse(<col1_name> == <val>, "<oth_val>", "<ref_val>")),
# as.factor(c("R", "<ref_val>")),
# ref="<ref_val>"),
# This doesn't work - use sapply instead
# <col_name>.fctr_num=grep(<col_name>, levels(<col_name>.fctr)),
#
# Date.my=as.Date(strptime(Date, "%m/%d/%y %H:%M")),
# Year=year(Date.my),
# Month=months(Date.my),
# Weekday=weekdays(Date.my)
# <col_name>=<table>[as.character(<col2_name>)],
# <col_name>=as.numeric(<col2_name>),
# <col_name> = trunc(<col2_name> / 100),
.rnorm = rnorm(n=nrow(obs_df))
)
# If levels of a factor are different across obs_df & glb_newobs_df; predict.glm fails
# Transformations not handled by mutate
# obs_df$<col_name>.fctr.num <- sapply(1:nrow(obs_df),
# function(row_ix) grep(obs_df[row_ix, "<col_name>"],
# levels(obs_df[row_ix, "<col_name>.fctr"])))
#print(summary(obs_df))
#print(sapply(names(obs_df), function(col) sum(is.na(obs_df[, col]))))
return(obs_df)
}
glb_allobs_df <- add_new_diag_feats(glb_allobs_df)## Loading required package: plyrrequire(dplyr)## Loading required package: dplyr
##
## Attaching package: 'dplyr'
##
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
##
## The following objects are masked from 'package:gdata':
##
## combine, first, last
##
## The following objects are masked from 'package:stats':
##
## filter, lag
##
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#stop(here"); sav_allobs_df <- glb_allobs_df # glb_allobs_df <- sav_allobs_df
# Merge some <descriptor>
# glb_allobs_df$<descriptor>.my <- glb_allobs_df$<descriptor>
# glb_allobs_df[grepl("\\bAIRPORT\\b", glb_allobs_df$<descriptor>.my),
# "<descriptor>.my"] <- "AIRPORT"
# Check distributions of newly transformed / extracted vars
# Enhancement: remove vars that were displayed ealier
dsp_numeric_feats_dstrb(feats_vctr=setdiff(names(glb_allobs_df),
c(myfind_chr_cols_df(glb_allobs_df), glb_rsp_var_raw, glb_rsp_var,
glb_exclude_vars_as_features)))## [1] "feat: biddable"
## [1] "feat: .rnorm"
# Convert factors to dummy variables
# Build splines require(splines); bsBasis <- bs(training$age, df=3)
#pairs(subset(glb_trnobs_df, select=-c(col_symbol)))
# Check for glb_newobs_df & glb_trnobs_df features range mismatches
# Other diagnostics:
# print(subset(glb_trnobs_df, <col1_name> == max(glb_trnobs_df$<col1_name>, na.rm=TRUE) &
# <col2_name> <= mean(glb_trnobs_df$<col1_name>, na.rm=TRUE)))
# print(glb_trnobs_df[which.max(glb_trnobs_df$<col_name>),])
# print(<col_name>_freq_glb_trnobs_df <- mycreate_tbl_df(glb_trnobs_df, "<col_name>"))
# print(which.min(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col_name>)))
# print(which.max(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>)[, 2]))
# print(table(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>))
# print(table(is.na(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(table(sign(glb_trnobs_df$<col1_name>), glb_trnobs_df$<col2_name>))
# print(mycreate_xtab_df(glb_trnobs_df, <col1_name>))
# print(mycreate_xtab_df(glb_trnobs_df, c(<col1_name>, <col2_name>)))
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mycreate_xtab_df(glb_trnobs_df, c("<col1_name>", "<col2_name>")))
# <col1_name>_<col2_name>_xtab_glb_trnobs_df[is.na(<col1_name>_<col2_name>_xtab_glb_trnobs_df)] <- 0
# print(<col1_name>_<col2_name>_xtab_glb_trnobs_df <-
# mutate(<col1_name>_<col2_name>_xtab_glb_trnobs_df,
# <col3_name>=(<col1_name> * 1.0) / (<col1_name> + <col2_name>)))
# print(mycreate_sqlxtab_df(glb_allobs_df, c("<col1_name>", "<col2_name>")))
# print(<col2_name>_min_entity_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>, glb_trnobs_df$<col2_name>, min, na.rm=TRUE)))
# print(<col1_name>_na_by_<col2_name>_arr <-
# sort(tapply(glb_trnobs_df$<col1_name>.NA, glb_trnobs_df$<col2_name>, mean, na.rm=TRUE)))
# Other plots:
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>"))
# print(myplot_box(df=glb_trnobs_df, ycol_names="<col1_name>", xcol_name="<col2_name>"))
# print(myplot_line(subset(glb_trnobs_df, Symbol %in% c("CocaCola", "ProcterGamble")),
# "Date.POSIX", "StockPrice", facet_row_colnames="Symbol") +
# geom_vline(xintercept=as.numeric(as.POSIXlt("2003-03-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1983-01-01")))
# )
# print(myplot_line(subset(glb_trnobs_df, Date.POSIX > as.POSIXct("2004-01-01")),
# "Date.POSIX", "StockPrice") +
# geom_line(aes(color=Symbol)) +
# coord_cartesian(xlim=c(as.POSIXct("1990-01-01"),
# as.POSIXct("2000-01-01"))) +
# coord_cartesian(ylim=c(0, 250)) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-09-01"))) +
# geom_vline(xintercept=as.numeric(as.POSIXlt("1997-11-01")))
# )
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
# print(myplot_scatter(glb_allobs_df, "<col1_name>", "<col2_name>", colorcol_name="<Pred.fctr>") +
# geom_point(data=subset(glb_allobs_df, <condition>),
# mapping=aes(x=<x_var>, y=<y_var>), color="red", shape=4, size=5) +
# geom_vline(xintercept=84))
glb_chunks_df <- myadd_chunk(glb_chunks_df, "scrub.data", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 2 inspect.data 2 0 0 20.630 29.214 8.584
## 3 scrub.data 2 1 1 29.214 NA NAStep 2.1: scrub data
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold
## 376
## [1] "numeric data w/ 0s in : "
## sold startprice.log10
## 197 31
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 753 0 0 0 0 0
## productline .grpid
## 0 NAfindOffendingCharacter <- function(x, maxStringLength=256){
print(x)
for (c in 1:maxStringLength){
offendingChar <- substr(x,c,c)
#print(offendingChar) #uncomment if you want the indiv characters printed
#the next character is the offending multibyte Character
}
}
# string_vector <- c("test", "Se\x96ora", "works fine")
# lapply(string_vector, findOffendingCharacter)
# lapply(glb_allobs_df$description[29], findOffendingCharacter)
dsp_hdlxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "Headline", glb_rsp_var)))
#dsp_hdlxtab("(1914)|(1939)")
dsp_catxtab <- function(str)
print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains=str), ],
c("Headline.pfx", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# dsp_catxtab("1914)|(1939)")
# dsp_catxtab("19(14|39|64):")
# dsp_catxtab("19..:")
# Merge some categories
# glb_allobs_df$myCategory <-
# plyr::revalue(glb_allobs_df$myCategory, c(
# "#Business Day#Dealbook" = "Business#Business Day#Dealbook",
# "#Business Day#Small Business" = "Business#Business Day#Small Business",
# "dummy" = "dummy"
# ))
# ctgry_xtab_df <- orderBy(reformulate(c("-", ".n")),
# mycreate_sqlxtab_df(glb_allobs_df,
# c("myCategory", "NewsDesk", "SectionName", "SubsectionName", glb_rsp_var)))
# myprint_df(ctgry_xtab_df)
# write.table(ctgry_xtab_df, paste0(glb_out_pfx, "ctgry_xtab.csv"),
# row.names=FALSE)
# ctgry_cast_df <- orderBy(~ -Y -NA, dcast(ctgry_xtab_df,
# myCategory + NewsDesk + SectionName + SubsectionName ~
# Popular.fctr, sum, value.var=".n"))
# myprint_df(ctgry_cast_df)
# write.table(ctgry_cast_df, paste0(glb_out_pfx, "ctgry_cast.csv"),
# row.names=FALSE)
# print(ctgry_sum_tbl <- table(glb_allobs_df$myCategory, glb_allobs_df[, glb_rsp_var],
# useNA="ifany"))
dsp_chisq.test <- function(...) {
sel_df <- glb_allobs_df[sel_obs(...) &
!is.na(glb_allobs_df$Popular), ]
sel_df$.marker <- 1
ref_df <- glb_allobs_df[!is.na(glb_allobs_df$Popular), ]
mrg_df <- merge(ref_df[, c(glb_id_var, "Popular")],
sel_df[, c(glb_id_var, ".marker")], all.x=TRUE)
mrg_df[is.na(mrg_df)] <- 0
print(mrg_tbl <- table(mrg_df$.marker, mrg_df$Popular))
print("Rows:Selected; Cols:Popular")
#print(mrg_tbl)
print(chisq.test(mrg_tbl))
}
# dsp_chisq.test(Headline.contains="[Ee]bola")
# dsp_chisq.test(Snippet.contains="[Ee]bola")
# dsp_chisq.test(Abstract.contains="[Ee]bola")
# print(mycreate_sqlxtab_df(glb_allobs_df[sel_obs(Headline.contains="[Ee]bola"), ],
# c(glb_rsp_var, "NewsDesk", "SectionName", "SubsectionName")))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName))
# print(table(glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# print(table(glb_allobs_df$NewsDesk, glb_allobs_df$SectionName, glb_allobs_df$SubsectionName))
# glb_allobs_df$myCategory.fctr <- as.factor(glb_allobs_df$myCategory)
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 788 0 0 0 0 0
## 1 116 0 3 11 11 74 76
## Unknown 1 1 2 0 0 128 0# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("AT&T", "Other")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("AT&T", "Other")),
"cellular"] <- "1"
# glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
# (glb_allobs_df$carrier %in% c("None")),
# c(glb_id_var, glb_rsp_var_raw, "description", "carrier", "cellular")]
glb_allobs_df[(glb_allobs_df$cellular %in% c("Unknown")) &
(glb_allobs_df$carrier %in% c("None")),
"cellular"] <- "0"
print(table(glb_allobs_df$cellular, glb_allobs_df$carrier, useNA="ifany"))##
## AT&T None Other Sprint T-Mobile Unknown Verizon
## 0 0 789 0 0 0 0 0
## 1 117 0 5 11 11 74 76
## Unknown 0 0 0 0 0 128 0Step 2.1: scrub data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "transform.data", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 3 scrub.data 2 1 1 29.214 31.942 2.728
## 4 transform.data 2 2 2 31.942 NA NA### Mapping dictionary
#sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_map_vars)) {
for (feat in glb_map_vars) {
map_df <- myimport_data(url=glb_map_urls[[feat]],
comment="map_df",
print_diagn=TRUE)
glb_allobs_df <- mymap_codes(glb_allobs_df, feat, names(map_df)[2],
map_df, map_join_col_name=names(map_df)[1],
map_tgt_col_name=names(map_df)[2])
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_map_vars)
}
### Forced Assignments
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (feat in glb_assign_vars) {
new_feat <- paste0(feat, ".my")
print(sprintf("Forced Assignments for: %s -> %s...", feat, new_feat))
glb_allobs_df[, new_feat] <- glb_allobs_df[, feat]
pairs <- glb_assign_pairs_lst[[feat]]
for (pair_ix in 1:length(pairs$from)) {
if (is.na(pairs$from[pair_ix]))
nobs <- nrow(filter(glb_allobs_df,
is.na(eval(parse(text=feat),
envir=glb_allobs_df)))) else
nobs <- sum(glb_allobs_df[, feat] == pairs$from[pair_ix])
#nobs <- nrow(filter(glb_allobs_df, is.na(Married.fctr))) ; print(nobs)
if ((is.na(pairs$from[pair_ix])) && (is.na(pairs$to[pair_ix])))
stop("what are you trying to do ???")
if (is.na(pairs$from[pair_ix]))
glb_allobs_df[is.na(glb_allobs_df[, feat]), new_feat] <-
pairs$to[pair_ix] else
glb_allobs_df[glb_allobs_df[, feat] == pairs$from[pair_ix], new_feat] <-
pairs$to[pair_ix]
print(sprintf(" %s -> %s for %s obs",
pairs$from[pair_ix], pairs$to[pair_ix], format(nobs, big.mark=",")))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, glb_assign_vars)
}
### Derivations using mapping functions
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
for (new_feat in glb_derive_vars) {
print(sprintf("Creating new feature: %s...", new_feat))
args_lst <- NULL
for (arg in glb_derive_lst[[new_feat]]$args)
args_lst[[arg]] <- glb_allobs_df[, arg]
glb_allobs_df[, new_feat] <- do.call(glb_derive_lst[[new_feat]]$mapfn, args_lst)
}## [1] "Creating new feature: startprice.log10.cut.fctr..."
## [1] "Creating new feature: descr.my..."#stop(here")
#hex_vctr <- c("\n", "\211", "\235", "\317", "\333")
hex_regex <- paste0(c("\n", "\211", "\235", "\317", "\333"), collapse="|")
for (obs_id in c(10029, 10948, 10136, 10178, 11514, 11904, 12157, 12210, 12659)) {
# tmp_str <- unlist(strsplit(glb_allobs_df[row_pos, "descr.my"], ""))
# glb_allobs_df[row_pos, "descr.my"] <- paste0(tmp_str[!tmp_str %in% hex_vctr],
# collapse="")
row_pos <- which(glb_allobs_df$UniqueID == obs_id)
# glb_allobs_df[row_pos, "descr.my"] <-
# gsub(hex_regex, " ", glb_allobs_df[row_pos, "descr.my"])
}Step 2.2: transform data
#```{r extract_features, cache=FALSE, eval=!is.null(glb_txt_vars)}
glb_chunks_df <- myadd_chunk(glb_chunks_df, "extract.features", major.inc=TRUE)## label step_major step_minor label_minor bgn end elapsed
## 4 transform.data 2 2 2 31.942 32.411 0.469
## 5 extract.features 3 0 0 32.411 NA NAextract.features_chunk_df <- myadd_chunk(NULL, "extract.features_bgn")## label step_major step_minor label_minor bgn end
## 1 extract.features_bgn 1 0 0 32.419 NA
## elapsed
## 1 NA# Create new features that help prediction
# <col_name>.lag.2 <- lag(zoo(glb_trnobs_df$<col_name>), -2, na.pad=TRUE)
# glb_trnobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
# <col_name>.lag.2 <- lag(zoo(glb_newobs_df$<col_name>), -2, na.pad=TRUE)
# glb_newobs_df[, "<col_name>.lag.2"] <- coredata(<col_name>.lag.2)
#
# glb_newobs_df[1, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df) - 1,
# "<col_name>"]
# glb_newobs_df[2, "<col_name>.lag.2"] <- glb_trnobs_df[nrow(glb_trnobs_df),
# "<col_name>"]
# glb_allobs_df <- mutate(glb_allobs_df,
# A.P.http=ifelse(grepl("http",Added,fixed=TRUE), 1, 0)
# )
#
# glb_trnobs_df <- mutate(glb_trnobs_df,
# )
#
# glb_newobs_df <- mutate(glb_newobs_df,
# )
# Convert dates to numbers
# typically, dates come in as chars;
# so this must be done before converting chars to factors
#stop(here"); sav_allobs_df <- glb_allobs_df #; glb_allobs_df <- sav_allobs_df
if (!is.null(glb_date_vars)) {
glb_allobs_df <- cbind(glb_allobs_df,
myextract_dates_df(df=glb_allobs_df, vars=glb_date_vars,
id_vars=glb_id_var, rsp_var=glb_rsp_var))
for (sfx in c("", ".POSIX"))
glb_exclude_vars_as_features <-
union(glb_exclude_vars_as_features,
paste(glb_date_vars, sfx, sep=""))
for (feat in glb_date_vars) {
glb_allobs_df <- orderBy(reformulate(paste0(feat, ".POSIX")), glb_allobs_df)
# print(myplot_scatter(glb_allobs_df, xcol_name=paste0(feat, ".POSIX"),
# ycol_name=glb_rsp_var, colorcol_name=glb_rsp_var))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >=
strptime("2012-12-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".POSIX"),
ycol_name=glb_rsp_var, colorcol_name=paste0(feat, ".wkend")))
# Create features that measure the gap between previous timestamp in the data
require(zoo)
z <- zoo(as.numeric(as.POSIXlt(glb_allobs_df[, paste0(feat, ".POSIX")])))
glb_allobs_df[, paste0(feat, ".zoo")] <- z
print(head(glb_allobs_df[, c(glb_id_var, feat, paste0(feat, ".zoo"))]))
print(myplot_scatter(glb_allobs_df[glb_allobs_df[, paste0(feat, ".POSIX")] >
strptime("2012-10-01", "%Y-%m-%d"), ],
xcol_name=paste0(feat, ".zoo"), ycol_name=glb_rsp_var,
colorcol_name=glb_rsp_var))
b <- zoo(, seq(nrow(glb_allobs_df)))
last1 <- as.numeric(merge(z-lag(z, -1), b, all=TRUE)); last1[is.na(last1)] <- 0
glb_allobs_df[, paste0(feat, ".last1.log")] <- log(1 + last1)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last1.log")] > 0, ],
ycol_names=paste0(feat, ".last1.log"),
xcol_name=glb_rsp_var))
last2 <- as.numeric(merge(z-lag(z, -2), b, all=TRUE)); last2[is.na(last2)] <- 0
glb_allobs_df[, paste0(feat, ".last2.log")] <- log(1 + last2)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last2.log")] > 0, ],
ycol_names=paste0(feat, ".last2.log"),
xcol_name=glb_rsp_var))
last10 <- as.numeric(merge(z-lag(z, -10), b, all=TRUE)); last10[is.na(last10)] <- 0
glb_allobs_df[, paste0(feat, ".last10.log")] <- log(1 + last10)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last10.log")] > 0, ],
ycol_names=paste0(feat, ".last10.log"),
xcol_name=glb_rsp_var))
last100 <- as.numeric(merge(z-lag(z, -100), b, all=TRUE)); last100[is.na(last100)] <- 0
glb_allobs_df[, paste0(feat, ".last100.log")] <- log(1 + last100)
print(gp <- myplot_box(df=glb_allobs_df[glb_allobs_df[,
paste0(feat, ".last100.log")] > 0, ],
ycol_names=paste0(feat, ".last100.log"),
xcol_name=glb_rsp_var))
glb_allobs_df <- orderBy(reformulate(glb_id_var), glb_allobs_df)
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
c(paste0(feat, ".zoo")))
# all2$last3 = as.numeric(merge(z-lag(z, -3), b, all = TRUE))
# all2$last5 = as.numeric(merge(z-lag(z, -5), b, all = TRUE))
# all2$last10 = as.numeric(merge(z-lag(z, -10), b, all = TRUE))
# all2$last20 = as.numeric(merge(z-lag(z, -20), b, all = TRUE))
# all2$last50 = as.numeric(merge(z-lag(z, -50), b, all = TRUE))
#
#
# # order table
# all2 = all2[order(all2$id),]
#
# ## fill in NAs
# # count averages
# na.avg = all2 %>% group_by(weekend, hour) %>% dplyr::summarise(
# last1=mean(last1, na.rm=TRUE),
# last3=mean(last3, na.rm=TRUE),
# last5=mean(last5, na.rm=TRUE),
# last10=mean(last10, na.rm=TRUE),
# last20=mean(last20, na.rm=TRUE),
# last50=mean(last50, na.rm=TRUE)
# )
#
# # fill in averages
# na.merge = merge(all2, na.avg, by=c("weekend","hour"))
# na.merge = na.merge[order(na.merge$id),]
# for(i in c("last1", "last3", "last5", "last10", "last20", "last50")) {
# y = paste0(i, ".y")
# idx = is.na(all2[[i]])
# all2[idx,][[i]] <- na.merge[idx,][[y]]
# }
# rm(na.avg, na.merge, b, i, idx, n, pd, sec, sh, y, z)
}
}
rm(last1, last10, last100)## Warning in rm(last1, last10, last100): object 'last1' not found## Warning in rm(last1, last10, last100): object 'last10' not found## Warning in rm(last1, last10, last100): object 'last100' not found# Create factors of string variables
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "factorize.str.vars"), major.inc=TRUE)## label step_major step_minor label_minor
## 1 extract.features_bgn 1 0 0
## 2 extract.features_factorize.str.vars 2 0 0
## bgn end elapsed
## 1 32.419 32.435 0.016
## 2 32.435 NA NA#stop(here"); sav_allobs_df <- glb_allobs_df; #glb_allobs_df <- sav_allobs_df
print(str_vars <- myfind_chr_cols_df(glb_allobs_df))## description condition cellular carrier color
## "description" "condition" "cellular" "carrier" "color"
## storage productline .src .grpid descr.my
## "storage" "productline" ".src" ".grpid" "descr.my"if (length(str_vars <- setdiff(str_vars,
c(glb_exclude_vars_as_features, glb_txt_vars))) > 0) {
for (var in str_vars) {
warning("Creating factors of string variable: ", var,
": # of unique values: ", length(unique(glb_allobs_df[, var])))
glb_allobs_df[, paste0(var, ".fctr")] <-
relevel(factor(glb_allobs_df[, var]),
names(which.max(table(glb_allobs_df[, var], useNA = "ifany"))))
}
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features, str_vars)
}## Warning: Creating factors of string variable: condition: # of unique
## values: 6## Warning: Creating factors of string variable: cellular: # of unique values:
## 3## Warning: Creating factors of string variable: carrier: # of unique values:
## 7## Warning: Creating factors of string variable: color: # of unique values: 5## Warning: Creating factors of string variable: storage: # of unique values:
## 5if (!is.null(glb_txt_vars)) {
require(foreach)
require(gsubfn)
require(stringr)
require(tm)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text"), major.inc=TRUE)
chk_pattern_freq <- function(rex_str, ignore.case=TRUE) {
match_mtrx <- str_extract_all(txt_vctr, regex(rex_str, ignore_case=ignore.case),
simplify=TRUE)
match_df <- as.data.frame(match_mtrx[match_mtrx != ""])
names(match_df) <- "pattern"
return(mycreate_sqlxtab_df(match_df, "pattern"))
}
# match_lst <- gregexpr("\\bok(?!ay)", txt_vctr[746], ignore.case = FALSE, perl=TRUE); print(match_lst)
dsp_pattern <- function(rex_str, ignore.case=TRUE, print.all=TRUE) {
match_lst <- gregexpr(rex_str, txt_vctr, ignore.case = ignore.case, perl=TRUE)
match_lst <- regmatches(txt_vctr, match_lst)
match_df <- data.frame(matches=sapply(match_lst,
function (elems) paste(elems, collapse="#")))
match_df <- subset(match_df, matches != "")
if (print.all)
print(match_df)
return(match_df)
}
dsp_matches <- function(rex_str, ix) {
print(match_pos <- gregexpr(rex_str, txt_vctr[ix], perl=TRUE))
print(str_sub(txt_vctr[ix], (match_pos[[1]] / 100) * 99 + 0,
(match_pos[[1]] / 100) * 100 + 100))
}
myapply_gsub <- function(...) {
if ((length_lst <- length(names(gsub_map_lst))) == 0)
return(txt_vctr)
for (ptn_ix in 1:length_lst) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
length(names(gsub_map_lst)), names(gsub_map_lst)[ptn_ix]))
txt_vctr <- gsub(names(gsub_map_lst)[ptn_ix], gsub_map_lst[[ptn_ix]],
txt_vctr, ...)
}
return(txt_vctr)
}
myapply_txtmap <- function(txt_vctr, ...) {
nrows <- nrow(glb_txt_map_df)
for (ptn_ix in 1:nrows) {
if ((ptn_ix %% 10) == 0)
print(sprintf("running gsub for %02d (of %02d): #%s#...", ptn_ix,
nrows, glb_txt_map_df[ptn_ix, "rex_str"]))
txt_vctr <- gsub(glb_txt_map_df[ptn_ix, "rex_str"],
glb_txt_map_df[ptn_ix, "rpl_str"],
txt_vctr, ...)
}
return(txt_vctr)
#print(txt_vctr <- glb_allobs_df[glb_allobs_df$UniqueID == 11329, "descr.my"])
#strsplit(txt_vctr, "")[[1]][1]
#ptn_ix <- 2; glb_txt_map_df[ptn_ix, ]
#gsub(glb_txt_map_df[ptn_ix, "rex_str"], glb_txt_map_df[ptn_ix, "rpl_str"], txt_vctr)
#print(match_lst <- gregexpr(glb_txt_map_df[ptn_ix, "rex_str"], txt_vctr))
#strsplit(glb_txt_map_df[ptn_ix, "rex_str"], "")[[1]]
}
chk.equal <- function(bgn, end) {
print(all.equal(sav_txt_lst[["Headline"]][bgn:end],
glb_txt_chr_lst[["Headline"]][bgn:end]))
}
dsp.equal <- function(bgn, end) {
print(sav_txt_lst[["Headline"]][bgn:end])
print(glb_txt_chr_lst[["Headline"]][bgn:end])
}
#sav_txt_lst <- glb_txt_chr_lst; all.equal(sav_txt_lst, glb_txt_chr_lst)
#all.equal(sav_txt_lst[["Headline"]][1:4200], glb_txt_chr_lst[["Headline"]][1:4200])
#chk.equal( 1, 100)
#dsp.equal(86, 90)
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
txt_map_filename <- paste0(glb_txt_munge_filenames_pfx, "map.csv")
if (!file.exists(txt_map_filename))
stop(txt_map_filename, " not found!")
glb_txt_map_df <- read.csv(txt_map_filename, comment.char="#", strip.white=TRUE)
glb_txt_chr_lst <- list();
print(sprintf("Building glb_txt_chr_lst..."))
glb_txt_chr_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_vctr <- glb_allobs_df[, txt_var]
names(txt_vctr) <- glb_allobs_df[, glb_id_var]
# myapply_txtmap shd be created as a tm_map::content_transformer ?
#print(glb_txt_map_df)
#txt_var=glb_txt_vars[3]; txt_vctr <- glb_txt_chr_lst[[txt_var]]
#print(rex_str <- glb_txt_map_df[3, "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rex_str == "\\bWall St\\.", "rex_str"])
#print(rex_str <- glb_txt_map_df[grepl("du Pont", glb_txt_map_df$rex_str), "rex_str"])
#print(rex_str <- glb_txt_map_df[glb_txt_map_df$rpl_str == "versus", "rex_str"])
#print(tmp_vctr <- grep(rex_str, txt_vctr, value=TRUE, ignore.case=FALSE))
#ret_lst <- regexec(rex_str, txt_vctr, ignore.case=FALSE); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
#gsub(rex_str, glb_txt_map_df[glb_txt_map_df$rex_str == rex_str, "rpl_str"], tmp_vctr, ignore.case=FALSE)
#grep("Hong Hong", txt_vctr, value=TRUE)
txt_vctr <- myapply_txtmap(txt_vctr, ignore.case=FALSE)
}
names(glb_txt_chr_lst) <- glb_txt_vars
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining OK in %s:", txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "(?<!(BO|HO|LO))OK(?!(E\\!|ED|IE|IN|S ))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "Ok(?!(a\\.|ay|in|ra|um))", ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
print(chk_pattern_freq(rex_str <- "(?<!( b| B| c| C| g| G| j| M| p| P| w| W| r| Z|\\(b|ar|bo|Bo|co|Co|Ew|gk|go|ho|ig|jo|kb|ke|Ke|ki|lo|Lo|mo|mt|no|No|po|ra|ro|sm|Sm|Sp|to|To))ok(?!(ay|bo|e |e\\)|e,|e\\.|eb|ed|el|en|er|es|ey|i |ie|in|it|ka|ke|ki|ly|on|oy|ra|st|u |uc|uy|yl|yo))",
ignore.case=FALSE))
match_df <- dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
for (row in row.names(match_df))
dsp_matches(rex_str, ix=as.numeric(row))
}
# txt_vctr <- glb_txt_chr_lst[[glb_txt_vars[1]]]
# print(chk_pattern_freq(rex_str <- "(?<!( b| c| C| p|\\(b|bo|co|lo|Lo|Sp|to|To))ok(?!(ay|e |e\\)|e,|e\\.|ed|el|en|es|ey|ie|in|on|ra))", ignore.case=FALSE))
# print(chk_pattern_freq(rex_str <- "ok(?!(ay|el|on|ra))", ignore.case=FALSE))
# dsp_pattern(rex_str, ignore.case=FALSE, print.all=FALSE)
# dsp_matches(rex_str, ix=8)
# substr(txt_vctr[86], 5613, 5620)
# substr(glb_allobs_df[301, "review"], 550, 650)
#stop(here"); sav_txt_lst <- glb_txt_chr_lst
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining Acronyms in %s:", txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
print(chk_pattern_freq(rex_str <- "([[:upper:]]\\.( *)){2,}", ignore.case=FALSE))
# Check for names
print(subset(chk_pattern_freq(rex_str <- "(([[:upper:]]+)\\.( *)){1}",
ignore.case=FALSE),
.n > 1))
# dsp_pattern(rex_str="(OK\\.( *)){1}", ignore.case=FALSE)
# dsp_matches(rex_str="(OK\\.( *)){1}", ix=557)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)(\\B)", ix=461)
#dsp_matches(rex_str="\\bR\\.I\\.P(\\.*)", ix=461)
#print(str_sub(txt_vctr[676], 10100, 10200))
#print(str_sub(txt_vctr[74], 1, -1))
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl("( |-)[[:upper:]]", pattern))))
print(" consider cleaning if relevant to problem domain; geography name; .n > 1")
#grep("New G", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Wins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
#stop(here"); sav_txt_lst <- glb_txt_chr_lst
for (txt_var in glb_txt_vars) {
re_str <- "\\b(N|S|E|W|C)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
print(orderBy(~ -.n +pattern, subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))))
#grep("N Weaver", txt_vctr, value=TRUE, ignore.case=FALSE)
}
for (txt_var in glb_txt_vars) {
re_str <- "\\b(North|South|East|West|Central)( |\\.)(\\w)+"
print(sprintf("Remaining #%s# terms in %s: ", re_str, txt_var))
txt_vctr <- glb_txt_chr_lst[[txt_var]]
if (nrow(filtered_df <- subset(chk_pattern_freq(re_str, ignore.case=FALSE),
grepl(".", pattern))) > 0)
print(orderBy(~ -.n +pattern, filtered_df))
#grep("Central (African|Bankers|Cast|Italy|Role|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("East (Africa|Berlin|London|Poland|Rivals|Spring)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("North (American|Korean|West)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("South (Pacific|Street)", txt_vctr, value=TRUE, ignore.case=FALSE)
#grep("St\\. Martins", txt_vctr, value=TRUE, ignore.case=FALSE)
}
find_cmpnd_wrds <- function(txt_vctr) {
# Enhancements:
# - arg should be txt_corpus instead of txt_vctr
txt_corpus <- Corpus(VectorSource(txt_vctr))
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=TRUE)
txt_corpus <- tm_map(txt_corpus, removePunctuation,
preserve_intra_word_dashes=TRUE, lazy=FALSE)
# Defaulting to Tf since TfIdf with normalize = TRUE throws a warning for empty docs
terms_mtrx <- as.matrix(TermDocumentMatrix(txt_corpus, control=list(weighting=weightTf)))
terms_df <- orderBy(~ -Tf, data.frame(term=dimnames(terms_mtrx)$Terms,
Tf=rowSums(terms_mtrx)))
cmpnd_df <- subset(terms_df, grepl("-", term))
if (nrow(cmpnd_df) == 0) {
print(" No compounded terms found")
return(FALSE)
}
txt_compound_filename <- paste0(glb_txt_munge_filenames_pfx, "compound.csv")
if (!file.exists(txt_compound_filename))
stop(txt_compound_filename, " not found!")
filter_df <- read.csv(txt_compound_filename, comment.char="#", strip.white=TRUE)
cmpnd_df$filter <- FALSE
for (row_ix in 1:nrow(filter_df))
cmpnd_df[!cmpnd_df$filter, "filter"] <-
grepl(filter_df[row_ix, "rex_str"],
cmpnd_df[!cmpnd_df$filter, "term"], ignore.case=TRUE)
cmpnd_df <- subset(cmpnd_df, !filter)
# Bug in tm_map(txt_corpus, removePunctuation, preserve_intra_word_dashes=TRUE) ???
# "net-a-porter" gets converted to "net-aporter"
#grep("net-a-porter", txt_vctr, ignore.case=TRUE, value=TRUE)
#grep("maser-laser", txt_vctr, ignore.case=TRUE, value=TRUE)
#txt_corpus[[which(grepl("net-a-porter", txt_vctr, ignore.case=TRUE))]]
#grep("\\b(across|longer)-(\\w)", cmpnd_df$term, ignore.case=TRUE, value=TRUE)
#grep("(\\w)-(affected|term)\\b", cmpnd_df$term, ignore.case=TRUE, value=TRUE)
print(sprintf("nrow(cmpnd_df): %d", nrow(cmpnd_df)))
myprint_df(cmpnd_df)
}
# This should be run after glb_txt_corpus_lst is created with tolower
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "process.text_reporting_compound_terms"), major.inc=FALSE)
for (txt_var in glb_txt_vars) {
print(sprintf("Remaining compound terms in %s: ", txt_var))
find_cmpnd_wrds(txt_vctr=glb_txt_chr_lst[[txt_var]])
#grep("thirty-five", txt_vctr, ignore.case=TRUE, value=TRUE)
#rex_str <- glb_txt_map_df[grepl("hirty", glb_txt_map_df$rex_str), "rex_str"]
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "build.corpus"), major.inc=TRUE)
get_txt_terms <- function(terms_TDM) {
terms_mtrx <- as.matrix(as.TermDocumentMatrix(terms_TDM))
terms_df <- data.frame(term=dimnames(terms_mtrx)$Terms, weight=rowSums(terms_mtrx),
freq=rowSums(terms_mtrx > 0))
terms_df$pos <- 1:nrow(terms_df)
terms_df$cor.y <-
cor(as.matrix(as.DocumentTermMatrix(terms_TDM))[glb_allobs_df$.src == "Train",],
as.numeric(glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var]),
use="pairwise.complete.obs")
terms_df$cor.y.abs <- abs(terms_df$cor.y)
for (cls in unique(glb_allobs_df[, glb_txt_cor_var])) {
if (!is.na(cls))
terms_df[, paste0("weight.", as.character(cls))] <-
colSums(t(terms_mtrx) *
as.numeric(!is.na(glb_allobs_df[, glb_txt_cor_var]) &
(glb_allobs_df[, glb_txt_cor_var] == cls))) else
terms_df[, paste0("weight.", as.character(cls))] <-
colSums(t(terms_mtrx) *
as.numeric(is.na(glb_allobs_df[, glb_txt_cor_var])))
}
# Check all calls to get_terms_DTM_terms to change returned order assumption
return(terms_df <- orderBy(~ -weight, terms_df))
}
#plt_full_df <- get_terms_DTM_terms(terms_DTM=glb_full_terms_DTM_lst[[txt_var]])
get_corpus_terms <- function(txt_corpus) {
return(terms_df <- get_txt_terms(terms_TDM=TermDocumentMatrix(txt_corpus,
control=glb_txt_terms_control)))
}
#stop(here"); glb_to_sav()
glb_txt_corpus_lst <- list()
print(sprintf("Building glb_txt_corpus_lst..."))
glb_txt_corpus_lst <- foreach(txt_var=glb_txt_vars) %dopar% {
# for (txt_var in glb_txt_vars) {
txt_corpus <- Corpus(VectorSource(glb_txt_chr_lst[[txt_var]]))
txt_corpus <- tm_map(txt_corpus, PlainTextDocument, lazy=FALSE)
txt_corpus <- tm_map(txt_corpus, content_transformer(tolower), lazy=FALSE) #nuppr
# removePunctuation does not replace with whitespace. Use a custom transformer ???
txt_corpus <- tm_map(txt_corpus, removePunctuation, lazy=TRUE) #npnct<chr_ix>
# txt-corpus <- tm_map(txt_corpus, content_transformer(function(x, pattern) gsub(pattern, "", x))
if (!is.null(glb_txt_stop_words[[txt_var]]))
txt_corpus <- tm_map(txt_corpus, removeWords, glb_txt_stop_words[[txt_var]],
lazy=FALSE)#, lazy=TRUE) #nstopwrds
#print("StoppedWords:"); stopped_words_TfIdf_df <- inspect_terms(txt_corpus)
#stopped_words_TfIdf_df[grepl("cond", stopped_words_TfIdf_df$term, ignore.case=TRUE), ]
#txt_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#which(txt_X_mtrx[, 211] > 0)
#glb_allobs_df[which(txt_X_mtrx[, 211] > 0), glb_txt_vars]
#txt_X_mtrx[2159, txt_X_mtrx[2159, ] > 0]
# txt_corpus <- tm_map(txt_corpus, stemDocument, "english", lazy=TRUE) #Done below
#txt_corpus <- tm_map(txt_corpus, content_transformer(stemDocument))
#print("StemmedWords:"); stemmed_words_TfIdf_df <- inspect_terms(txt_corpus)
#stemmed_words_TfIdf_df[grepl("cond", stemmed_words_TfIdf_df$term, ignore.case=TRUE), ]
#stm_X_mtrx <- as.matrix(DocumentTermMatrix(txt_corpus, control=list(weighting=weightTfIdf)))
#glb_allobs_df[which((stm_X_mtrx[, 180] > 0) | (stm_X_mtrx[, 181] > 0)), glb_txt_vars]
#glb_allobs_df[which((stm_X_mtrx[, 181] > 0)), glb_txt_vars]
# glb_txt_corpus_lst[[txt_var]] <- txt_corpus
}
names(glb_txt_corpus_lst) <- glb_txt_vars
mycombineSynonyms <- content_transformer(function(x, syn=NULL) {
Reduce(function(a,b) {
gsub(paste0("\\b(", paste(b$syns, collapse="|"),")\\b"), b$word, a)}, syn, x)
})
#stop(here"); glb_to_sav(); sav_txt_corpus <- glb_txt_corpus_lst[[txt_var]]; all.equal(sav_txt_corpus, glb_txt_corpus_lst[[txt_var]]); glb_txt_corpus_lst[[txt_var]] <- sav_txt_corpus
glb_post_stop_words_terms_df_lst <- list();
glb_post_stop_words_terms_mtrx_lst <- list();
glb_post_stem_words_terms_df_lst <- list();
glb_post_stem_words_terms_mtrx_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf(" Top_n stop term weights for %s:", txt_var))
# This impacts stemming probably due to lazy parameter
print(myprint_df(full_terms_df <-
get_corpus_terms(txt_corpus=glb_txt_corpus_lst[[txt_var]]),
glb_txt_top_n[[txt_var]]))
glb_post_stop_words_terms_df_lst[[txt_var]] <- full_terms_df
terms_stop_mtrx <- as.matrix(DocumentTermMatrix(glb_txt_corpus_lst[[txt_var]],
control=glb_txt_terms_control))
rownames(terms_stop_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stop_words_terms_mtrx_lst[[txt_var]] <- terms_stop_mtrx
tmp_allobs_df <- glb_allobs_df[, c(glb_id_var, glb_rsp_var)]
tmp_allobs_df$terms.post.stop.n <- rowSums(terms_stop_mtrx > 0)
tmp_allobs_df$terms.post.stop.n.log <- log(1 + tmp_allobs_df$terms.post.stop.n)
tmp_allobs_df$weight.post.stop.sum <- rowSums(terms_stop_mtrx)
print(sprintf(" Top_n stem term weights for %s:", txt_var))
glb_txt_corpus_lst[[txt_var]] <- tm_map(glb_txt_corpus_lst[[txt_var]], stemDocument,
"english", lazy=FALSE)
if (!is.null(glb_txt_synonyms[[txt_var]])) {
syn_lst <- myrmNullObj(glb_txt_synonyms[[txt_var]])
glb_txt_corpus_lst[[txt_var]] <- tm_map(glb_txt_corpus_lst[[txt_var]],
mycombineSynonyms,
syn_lst, lazy=FALSE)
}
print(myprint_df(full_terms_df <- get_corpus_terms(glb_txt_corpus_lst[[txt_var]]),
glb_txt_top_n[[txt_var]]))
glb_post_stem_words_terms_df_lst[[txt_var]] <- full_terms_df
terms_stem_mtrx <- as.matrix(DocumentTermMatrix(glb_txt_corpus_lst[[txt_var]],
control=glb_txt_terms_control))
rownames(terms_stem_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
glb_post_stem_words_terms_mtrx_lst[[txt_var]] <- terms_stem_mtrx
tmp_allobs_df$terms.post.stem.n <- rowSums(terms_stem_mtrx > 0)
tmp_allobs_df$terms.post.stem.n.log <- log(1 + tmp_allobs_df$terms.post.stem.n)
tmp_allobs_df$weight.post.stem.sum <- rowSums(terms_stem_mtrx)
tmp_allobs_df$terms.n.stem.stop.Ratio <-
1.0 * tmp_allobs_df$terms.post.stem.n / tmp_allobs_df$terms.post.stop.n
tmp_allobs_df[(is.nan(tmp_allobs_df$terms.n.stem.stop.Ratio) |
is.infinite(tmp_allobs_df$terms.n.stem.stop.Ratio)),
"terms.n.stem.stop.Ratio"] <- 1.0
if ((n.errors <- sum(tmp_allobs_df$terms.n.stem.stop.Ratio > 1)) > 0)
stop(n.errors, " obs in tmp_allobs_df have terms.n.stem.stop.Ratio > 1",
" happening due to terms filtered by glb_txt_terms_control$bounds$global[1] but stemmable to other terms")
#print(head(subset(tmp_allobs_df, terms.n.stem.stop.Ratio > 1)))
#glb_allobs_df[(row_ix <- which(glb_allobs_df$UniqueID == 10465)), ]
#terms_stop_mtrx[row_ix, terms_stop_mtrx[row_ix, ] > 0]
#setdiff(names(terms_stem_mtrx[row_ix, terms_stem_mtrx[row_ix, ] > 0]), names(terms_stop_mtrx[row_ix, terms_stop_mtrx[row_ix, ] > 0]))
#mydsp_obs(list(descr.my.contains="updat"))
tmp_allobs_df$weight.sum.stem.stop.Ratio <-
1.0 * tmp_allobs_df$weight.post.stem.sum / tmp_allobs_df$weight.post.stop.sum
tmp_allobs_df[is.nan(tmp_allobs_df$weight.sum.stem.stop.Ratio) |
is.infinite(tmp_allobs_df$weight.sum.stem.stop.Ratio),
"weight.sum.stem.stop.Ratio"] <- 1.0
tmp_trnobs_df <- tmp_allobs_df[!is.na(tmp_allobs_df[, glb_rsp_var]), ]
print(cor(as.matrix(tmp_trnobs_df[, -c(1, 2)]),
as.numeric(tmp_trnobs_df[, glb_rsp_var])))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
tmp_allobs_df <- tmp_allobs_df[, -c(1, 2)]
names(tmp_allobs_df) <- paste(paste0(txt_var_pfx, "."), names(tmp_allobs_df),
sep="")
glb_allobs_df <- cbind(glb_allobs_df, tmp_allobs_df)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
paste(paste0(txt_var_pfx, ".terms.post."), c("stop.n", "stem.n"), sep=""))
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "extract.DTM"), major.inc=TRUE)
#stop(here")
glb_full_DTM_lst <- list(); glb_sprs_DTM_lst <- list();
for (txt_var in glb_txt_vars) {
print(sprintf("Extracting term weights for %s...", txt_var))
txt_corpus <- glb_txt_corpus_lst[[txt_var]]
full_DTM <- DocumentTermMatrix(txt_corpus,
control=glb_txt_terms_control)
sprs_DTM <- removeSparseTerms(full_DTM,
glb_sprs_thresholds[txt_var])
glb_full_DTM_lst[[txt_var]] <- full_DTM
glb_sprs_DTM_lst[[txt_var]] <- sprs_DTM
}
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "report.DTM"), major.inc=TRUE)
require(reshape2)
for (txt_var in glb_txt_vars) {
print(sprintf("Reporting term weights for %s...", txt_var))
full_DTM <- glb_full_DTM_lst[[txt_var]]
sprs_DTM <- glb_sprs_DTM_lst[[txt_var]]
print(" Full TermMatrix:"); print(full_DTM)
full_terms_df <- get_txt_terms(full_DTM)
# full_terms_df <- full_terms_df[, c(2, 1, 3, 4)]
# col_names <- names(full_terms_df)
# col_names[2:length(col_names)] <-
# paste(col_names[2:length(col_names)], ".full", sep="")
# names(full_terms_df) <- col_names
print(" Sparse TermMatrix:"); print(sprs_DTM)
sprs_terms_df <- get_txt_terms(sprs_DTM)
# sprs_terms_df <- sprs_terms_df[, c(2, 1, 3, 4)]
# col_names <- names(sprs_terms_df)
# col_names[2:length(col_names)] <-
# paste(col_names[2:length(col_names)], ".sprs", sep="")
# names(sprs_terms_df) <- col_names
intersect(names(full_terms_df), names(sprs_terms_df))
terms_df <- merge(full_terms_df, sprs_terms_df,
by=setdiff(intersect(names(full_terms_df), names(sprs_terms_df)), "pos"),
all.x=TRUE, suffixes=c(".full", ".sprs"))
terms_df$in.sprs <- !is.na(terms_df$pos.sprs)
plt_terms_df <- subset(terms_df,
weight >= min(terms_df$weight[!is.na(terms_df$pos.sprs)], na.rm=TRUE))
plt_terms_df$label <- ""
plt_terms_df[is.na(plt_terms_df$pos.sprs), "label"] <-
plt_terms_df[is.na(plt_terms_df$pos.sprs), "term"]
# glb_important_terms[[txt_var]] <- union(glb_important_terms[[txt_var]],
# plt_terms_df[is.na(plt_terms_df$TfIdf.sprs), "term"])
print(myplot_scatter(plt_terms_df, "freq", "weight",
colorcol_name="in.sprs") +
geom_text(aes(label=label), color="Black", size=3.5))
melt_terms_df <- orderBy(~ -value,
melt(terms_df, id.vars="term", measure.vars = c("weight", "freq")))
print(ggplot(melt_terms_df, aes(value, color=variable)) + stat_ecdf() +
geom_hline(yintercept=glb_sprs_thresholds[txt_var],
linetype = "dotted"))
melt_terms_df <- orderBy(~ -value,
melt(subset(terms_df, in.sprs), id.vars="term",
measure.vars=grep("weight.", names(terms_df), value=TRUE)))
print(myplot_hbar(melt_terms_df, "term", "value", colorcol_name="variable"))
melt_terms_df <- orderBy(~ -value,
melt(subset(terms_df, !in.sprs), id.vars="term",
measure.vars=grep("weight.", names(terms_df), value=TRUE)))
print(myplot_hbar(head(melt_terms_df, glb_txt_top_n[[txt_var]]), "term", "value",
colorcol_name="variable"))
}
# sav_full_DTM_lst <- glb_full_DTM_lst
# print(identical(sav_glb_txt_corpus_lst, glb_txt_corpus_lst))
# print(all.equal(length(sav_glb_txt_corpus_lst), length(glb_txt_corpus_lst)))
# print(all.equal(names(sav_glb_txt_corpus_lst), names(glb_txt_corpus_lst)))
# print(all.equal(sav_glb_txt_corpus_lst[["Headline"]], glb_txt_corpus_lst[["Headline"]]))
# print(identical(sav_full_DTM_lst, glb_full_DTM_lst))
rm(full_terms_mtrx)
# Create txt features
if ((length(glb_txt_vars) > 1) &&
(length(unique(pfxs <- sapply(glb_txt_vars,
function(txt) toupper(substr(txt, 1, 1))))) < length(glb_txt_vars)))
stop("Prefixes for corpus freq terms not unique: ", pfxs)
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DTM"),
major.inc=TRUE)
#stop(here"); glb_to_sav(); all.equal(sav_allobs_df, glb_allobs_df); glb_allobs_df <- sav_allobs_df
require(tidyr)
for (txt_var in glb_txt_vars) {
print(sprintf("Binding DTM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_X_df <- as.data.frame(as.matrix(glb_full_DTM_lst[[txt_var]]))
terms_full_df <- get_txt_terms(glb_full_DTM_lst[[txt_var]])
colnames(txt_full_X_df) <- paste(txt_var_pfx, ".T.",
make.names(colnames(txt_full_X_df)), sep="")
rownames(txt_full_X_df) <- rownames(glb_allobs_df) # warning otherwise
plt_full_df <- terms_full_df
names(plt_full_df)[grepl("weight$", names(plt_full_df))] <- "weight.all"
# gather(plt_full_df[1:5, ], domain, TfIdf, -matches("!(TfIdf)"))
# gather(plt_full_df[1:5, grepl("TfIdf", names(plt_full_df))], domain, TfIdf)
# gather(plt_full_df[1:5, ], domain, TfIdf,
# -names(plt_full_df)[!grepl("TfIdf", names(plt_full_df))])
plt_full_df <- gather(plt_full_df, domain, weight,
-c(term, freq, pos, cor.y, cor.y.abs))
plt_full_df$label <- NA
top_val_terms <- orderBy(~-weight, terms_full_df)$term[1:glb_txt_top_n[[txt_var]]]
plt_full_df[plt_full_df$term %in% top_val_terms, "label"] <-
plt_full_df[plt_full_df$term %in% top_val_terms, "term"]
top_cor_terms <- orderBy(~-cor.y.abs,
terms_full_df)$term[1:glb_txt_top_n[[txt_var]]]
plt_full_df[plt_full_df$term %in% top_cor_terms, "label"] <-
plt_full_df[plt_full_df$term %in% top_cor_terms, "term"]
#plt_full_df$type <- "none"
plt_full_df[plt_full_df$term %in% top_val_terms, "type"] <- "top.weight"
plt_full_df[plt_full_df$term %in% top_cor_terms, "type"] <- "top.cor"
plt_full_df[plt_full_df$term %in% intersect(top_val_terms, top_cor_terms), "type"] <-
"top.both"
cor.y.rnorm <- cor(glb_allobs_df$.rnorm, glb_allobs_df[, glb_rsp_var],
use="pairwise.complete.obs")
print(ggplot(plt_full_df, aes(x=weight, y=cor.y)) + facet_wrap(~ domain) +
geom_point(aes(size=freq), color="grey") +
geom_jitter() +
geom_text(aes(label=label, color=type), size=3.5) +
#geom_hline(yintercept=cor.y.rnorm, color="red") +
geom_hline(yintercept=c(cor.y.rnorm, -cor.y.rnorm), color="red"))
if (glb_txt_terms_filter == "sparse") {
txt_X_df <- as.data.frame(as.matrix(glb_sprs_DTM_lst[[txt_var]]))
select_terms <- make.names(colnames(txt_X_df))
# colnames(txt_X_df) <- paste(txt_var_pfx, ".T.",
# make.names(colnames(txt_X_df)), sep="")
# rownames(txt_X_df) <- rownames(glb_allobs_df) # warning otherwise
} else if (glb_txt_terms_filter == "top.val") {
select_terms <- orderBy(~-weight,
terms_full_df)$term[1:glb_txt_top_n[[txt_var]]]
# txt_X_df <- txt_full_X_df[, subset(terms_full_df, term %in% select_terms)$pos,
# FALSE]
} else if (glb_txt_terms_filter == "top.cor") {
select_terms <- orderBy(~-cor.y.abs,
terms_full_df)$term[1:glb_txt_top_n[[txt_var]]]
# txt_X_df <- txt_full_X_df[, subset(terms_full_df, term %in% select_terms)$pos,
# FALSE]
} else if (glb_txt_terms_filter == "union.top.val.cor") {
select_terms <- union(
orderBy(~-weight , terms_full_df)$term[1:glb_txt_top_n[[txt_var]]],
orderBy(~-cor.y.abs, terms_full_df)$term[1:glb_txt_top_n[[txt_var]]])
} else stop(
"glb_txt_terms_filter should be one of c('sparse', 'top.val', 'top.cor', 'union.top.val.cor') vs. '",
glb_txt_terms_filter, "'")
assoc_terms_lst <- findAssocs(glb_full_DTM_lst[[txt_var]], select_terms,
glb_txt_assoc_cor[[txt_var]])
assoc_terms <- c(NULL)
for (term in names(assoc_terms_lst))
if (length(assoc_terms_lst[[term]]) > 0)
assoc_terms <- union(assoc_terms, names(assoc_terms_lst[[term]]))
txt_X_df <- txt_full_X_df[,
subset(terms_full_df, term %in% c(select_terms, assoc_terms))$pos,
FALSE]
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df) # TfIdf is normalized
#glb_allobs_df <- cbind(glb_allobs_df, log_X_df) # if using non-normalized metrics
}
#identical(chk_entity_df, glb_allobs_df)
#chk_entity_df <- glb_allobs_df
extract.features_chunk_df <- myadd_chunk(extract.features_chunk_df,
paste0("extract.features_", "bind.DXM"),
major.inc=TRUE)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
glb_punct_vctr <- c("!", "\"", "#", "\\$", "%", "&", "'",
"\\(|\\)",# "\\(", "\\)",
"\\*", "\\+", ",", "-", "\\.", "/", ":", ";",
"<|>", # "<",
"=",
# ">",
"\\?", "@", "\\[", "\\\\", "\\]", "\\^", "_", "`",
"\\{", "\\|", "\\}", "~")
txt_X_df <- glb_allobs_df[, c(glb_id_var, ".rnorm"), FALSE]
txt_X_df <- foreach(txt_var=glb_txt_vars, .combine=cbind) %dopar% {
#for (txt_var in glb_txt_vars) {
print(sprintf("Binding DXM for %s...", txt_var))
txt_var_pfx <- toupper(substr(txt_var, 1, 1))
txt_full_DTM_mtrx <- as.matrix(glb_full_DTM_lst[[txt_var]])
rownames(txt_full_DTM_mtrx) <- rownames(glb_allobs_df) # print undreadable otherwise
#print(txt_full_DTM_mtrx[txt_full_DTM_mtrx[, "ebola"] != 0, "ebola"])
# Create <txt_var>.T.<term> for glb_important_terms
for (term in glb_important_terms[[txt_var]])
txt_X_df[, paste0(txt_var_pfx, ".T.", make.names(term))] <-
txt_full_DTM_mtrx[, term]
# Create <txt_var>.wrds.n.log & .wrds.unq.n.log
txt_X_df[, paste0(txt_var_pfx, ".wrds.n.log")] <-
log(1 + mycount_pattern_occ("\\w+", glb_txt_chr_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".wrds.unq.n.log")] <-
log(1 + rowSums(txt_full_DTM_mtrx != 0))
txt_X_df[, paste0(txt_var_pfx, ".weight.sum")] <-
rowSums(txt_full_DTM_mtrx)
txt_X_df[, paste0(txt_var_pfx, ".ratio.weight.sum.wrds.n")] <-
txt_X_df[, paste0(txt_var_pfx, ".weight.sum")] /
(exp(txt_X_df[, paste0(txt_var_pfx, ".wrds.n.log")]) - 1)
txt_X_df[is.nan(txt_X_df[, paste0(txt_var_pfx, ".ratio.weight.sum.wrds.n")]),
paste0(txt_var_pfx, ".ratio.weight.sum.wrds.n")] <- 0
# Create <txt_var>.chrs.n.log
txt_X_df[, paste0(txt_var_pfx, ".chrs.n.log")] <-
log(1 + mycount_pattern_occ(".", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".chrs.uppr.n.log")] <-
log(1 + mycount_pattern_occ("[[:upper:]]", glb_allobs_df[, txt_var]))
txt_X_df[, paste0(txt_var_pfx, ".dgts.n.log")] <-
log(1 + mycount_pattern_occ("[[:digit:]]", glb_allobs_df[, txt_var]))
# Create <txt_var>.npnct?.log
# would this be faster if it's iterated over each row instead of
# each created column ???
for (punct_ix in 1:length(glb_punct_vctr)) {
# smp0 <- " "
# smp1 <- "! \" # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _ ` { | } ~"
# smp2 <- paste(smp1, smp1, sep=" ")
# print(sprintf("Testing %s pattern:", glb_punct_vctr[punct_ix]))
# results <- mycount_pattern_occ(glb_punct_vctr[punct_ix], c(smp0, smp1, smp2))
# names(results) <- NULL; print(results)
txt_X_df[,
paste0(txt_var_pfx, ".chrs.pnct", sprintf("%02d", punct_ix), ".n.log")] <-
log(1 + mycount_pattern_occ(glb_punct_vctr[punct_ix],
glb_allobs_df[, txt_var]))
}
# print(head(glb_allobs_df[glb_allobs_df[, "A.npnct23.log"] > 0,
# c("UniqueID", "Popular", "Abstract", "A.npnct23.log")]))
# Create <txt_var>.wrds.stop.n.log & <txt_var>ratio.wrds.stop.n.wrds.n
if (!is.null(glb_txt_stop_words[[txt_var]])) {
stop_words_rex_str <- paste0("\\b(",
paste0(glb_txt_stop_words[[txt_var]], collapse="|"),
")\\b")
txt_X_df[, paste0(txt_var_pfx, ".wrds.stop.n", ".log")] <-
log(1 + mycount_pattern_occ(stop_words_rex_str, glb_txt_chr_lst[[txt_var]]))
txt_X_df[, paste0(txt_var_pfx, ".ratio.wrds.stop.n.wrds.n")] <-
exp(txt_X_df[, paste0(txt_var_pfx, ".wrds.stop.n", ".log")] -
txt_X_df[, paste0(txt_var_pfx, ".wrds.n", ".log")])
}
# Create <txt_var>.P.http
txt_X_df[, paste(txt_var_pfx, ".P.http", sep="")] <-
as.integer(0 + mycount_pattern_occ("http", glb_allobs_df[, txt_var]))
# Create <txt_var>.P.mini & air
txt_X_df[, paste(txt_var_pfx, ".P.mini", sep="")] <-
as.integer(0 + mycount_pattern_occ("mini(?!m)", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.air", sep="")] <-
as.integer(0 + mycount_pattern_occ("(?<![fhp])air", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.black", sep="")] <-
as.integer(0 + mycount_pattern_occ("black", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.white", sep="")] <-
as.integer(0 + mycount_pattern_occ("white", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.gold", sep="")] <-
as.integer(0 + mycount_pattern_occ("gold", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df[, paste(txt_var_pfx, ".P.spacegray", sep="")] <-
as.integer(0 + mycount_pattern_occ("spacegray", glb_allobs_df[, txt_var],
perl=TRUE))
txt_X_df <- subset(txt_X_df, select=-.rnorm)
txt_X_df <- txt_X_df[, -grep(glb_id_var, names(txt_X_df), fixed=TRUE), FALSE]
#glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
}
glb_allobs_df <- cbind(glb_allobs_df, txt_X_df)
#myplot_box(glb_allobs_df, "A.sum.TfIdf", glb_rsp_var)
# if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
# stop("Why is this happening ?")
# Generate summaries
# print(summary(glb_allobs_df))
# print(sapply(names(glb_allobs_df), function(col) sum(is.na(glb_allobs_df[, col]))))
# print(summary(glb_trnobs_df))
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(summary(glb_newobs_df))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
glb_exclude_vars_as_features <- union(glb_exclude_vars_as_features,
glb_txt_vars)
rm(log_X_df, txt_X_df)
}## Loading required package: stringr## label step_major step_minor label_minor
## 2 extract.features_factorize.str.vars 2 0 0
## 3 extract.features_process.text 3 0 0
## bgn end elapsed
## 2 32.435 32.495 0.06
## 3 32.496 NA NA
## [1] "Building glb_txt_chr_lst..."
## [1] "running gsub for 10 (of 179): #\\bCentral African Republic\\b#..."
## [1] "running gsub for 20 (of 179): #\\bAlejandro G\\. Iñárritu#..."
## [1] "running gsub for 30 (of 179): #\\bC\\.A\\.A\\.#..."
## [1] "running gsub for 40 (of 179): #\\bCV\\.#..."
## [1] "running gsub for 50 (of 179): #\\bE\\.P\\.A\\.#..."
## [1] "running gsub for 60 (of 179): #\\bG\\.I\\. Joe#..."
## [1] "running gsub for 70 (of 179): #\\bISIS\\.#..."
## [1] "running gsub for 80 (of 179): #\\bJ\\.K\\. Simmons#..."
## [1] "running gsub for 90 (of 179): #\\bM\\. Henri Pol#..."
## [1] "running gsub for 100 (of 179): #\\bN\\.Y\\.S\\.E\\.#..."
## [1] "running gsub for 110 (of 179): #\\bR\\.B\\.S\\.#..."
## [1] "running gsub for 120 (of 179): #\\bSteven A\\. Cohen#..."
## [1] "running gsub for 130 (of 179): #\\bV\\.A\\.#..."
## [1] "running gsub for 140 (of 179): #\\bWall Street#..."
## [1] "running gsub for 150 (of 179): #\\bSaint( |-)((Laurent|Lucia)\\b)+#..."
## [1] "running gsub for 160 (of 179): #\\bSouth( |\\\\.)(America|American|Africa|African|Carolina|Dakota|Korea|Korean|Sudan)\\b#..."
## [1] "running gsub for 170 (of 179): #(\\w)-a-year#..."
## [1] "Remaining OK in descr.my:"
## pattern .n
## 1 OK 1
## [[1]]
## [1] NA
## attr(,"match.length")
## [1] NA
##
## [1] NA
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## [1] "Remaining Acronyms in descr.my:"
## [1] pattern .n
## <0 rows> (or 0-length row.names)
## pattern .n
## 1 CONDITION. 5
## 2 ICLOUD. 2
## 3 USE. 2
## [1] "Remaining #\\b(Fort|Ft\\.|Hong|Las|Los|New|Puerto|Saint|San|St\\.)( |-)(\\w)+# terms in descr.my: "
## pattern .n
## 2 New Screen 1
## [1] " consider cleaning if relevant to problem domain; geography name; .n > 1"
## [1] "Remaining #\\b(N|S|E|W|C)( |\\.)(\\w)+# terms in descr.my: "
## pattern .n
## 1 W blue 1
## [1] "Remaining #\\b(North|South|East|West|Central)( |\\.)(\\w)+# terms in descr.my: "
## label step_major
## 3 extract.features_process.text 3
## 4 extract.features_process.text_reporting_compound_terms 3
## step_minor label_minor bgn end elapsed
## 3 0 0 32.496 33.33 0.834
## 4 1 1 33.330 NA NA
## [1] "Remaining compound terms in descr.my: "
## [1] " No compounded terms found"
## label step_major
## 4 extract.features_process.text_reporting_compound_terms 3
## 5 extract.features_build.corpus 4
## step_minor label_minor bgn end elapsed
## 4 1 1 33.330 35.428 2.099
## 5 0 0 35.429 NA NA
## [1] "Building glb_txt_corpus_lst..."
## [1] " Top_n stop term weights for descr.my:"## Warning in weighting(x): empty document(s): character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
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## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## Warning in cor(as.matrix(as.DocumentTermMatrix(terms_TDM))[glb_allobs_df
## $.src == : the standard deviation is zero## [1] "Rows: 177; Cols: 9"
## term weight freq pos cor.y cor.y.abs
## condit condit 104.99189 162 40 -0.066484434 0.066484434
## use use 85.98543 129 168 0.047049552 0.047049552
## new new 66.17058 54 103 0.019384025 0.019384025
## screen screen 62.95334 86 140 -0.106844601 0.106844601
## scratch scratch 62.68763 105 139 0.006098563 0.006098563
## good good 60.85429 66 78 -0.096553969 0.096553969
## weight.(-0.352,1.3] weight.(1.3,2.95] weight.(-2,-0.352]
## condit 26.29079 75.31182 3.389277
## use 14.09108 70.44050 1.453840
## new 13.80850 50.11853 2.243548
## screen 27.79115 33.63425 1.527939
## scratch 9.95466 50.87938 1.853589
## good 19.20397 38.63705 3.013269
## term weight freq pos cor.y cor.y.abs
## damag damag 11.334256 14 47 -0.04071014 0.04071014
## bought bought 10.188007 4 26 0.01308947 0.01308947
## factori factori 9.968234 9 63 0.02753494 0.02753494
## 16gb 16gb 8.015036 8 2 0.04545899 0.04545899
## esn esn 4.988093 3 61 -0.04994372 0.04994372
## 2016 2016 3.174241 3 3 -0.04994372 0.04994372
## weight.(-0.352,1.3] weight.(1.3,2.95] weight.(-2,-0.352]
## damag 5.4234724 4.302126 1.608657
## bought 4.1209916 6.067015 0.000000
## factori 2.0626836 7.905551 0.000000
## 16gb 1.9398169 6.075219 0.000000
## esn 0.8657021 4.122391 0.000000
## 2016 3.1742409 0.000000 0.000000
## term weight freq pos cor.y cor.y.abs
## addit addit 3.280736 3 7 -0.07056423 0.07056423
## 2016 2016 3.174241 3 3 -0.04994372 0.04994372
## warranti warranti 3.174241 3 170 -0.04994372 0.04994372
## batteri batteri 3.095541 3 21 -0.05635477 0.05635477
## complet complet 2.830180 3 39 0.02862448 0.02862448
## associ associ 2.789484 3 17 0.01970363 0.01970363
## weight.(-0.352,1.3] weight.(1.3,2.95] weight.(-2,-0.352]
## addit 2.044019 1.2367172 0
## 2016 3.174241 0.0000000 0
## warranti 3.174241 0.0000000 0
## batteri 2.308539 0.7870019 0
## complet 0.000000 2.8301798 0
## associ 0.000000 2.7894844 0
## term weight freq pos cor.y cor.y.abs
## addit addit 3.280736 3 7 -0.07056423 0.07056423
## 2016 2016 3.174241 3 3 -0.04994372 0.04994372
## warranti warranti 3.174241 3 170 -0.04994372 0.04994372
## batteri batteri 3.095541 3 21 -0.05635477 0.05635477
## complet complet 2.830180 3 39 0.02862448 0.02862448
## associ associ 2.789484 3 17 0.01970363 0.01970363
## weight.(-0.352,1.3] weight.(1.3,2.95] weight.(-2,-0.352]
## addit 2.044019 1.2367172 0
## 2016 3.174241 0.0000000 0
## warranti 3.174241 0.0000000 0
## batteri 2.308539 0.7870019 0
## complet 0.000000 2.8301798 0
## associ 0.000000 2.7894844 0## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## [,1]
## terms.post.stop.n -0.10786800
## terms.post.stop.n.log -0.11456605
## weight.post.stop.sum -0.12532964
## terms.post.stem.n -0.10985574
## terms.post.stem.n.log -0.11534651
## weight.post.stem.sum -0.12720218
## terms.n.stem.stop.Ratio -0.01919407
## weight.sum.stem.stop.Ratio 0.03625058
## label step_major step_minor label_minor bgn
## 5 extract.features_build.corpus 4 0 0 35.429
## 6 extract.features_extract.DTM 5 0 0 41.007
## end elapsed
## 5 41.007 5.578
## 6 NA NA
## [1] "Extracting term weights for descr.my..."## Warning in weighting(x): empty document(s): character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) character(0) character(0) character(0) character(0)
## character(0) charact## label step_major step_minor label_minor bgn
## 6 extract.features_extract.DTM 5 0 0 41.007
## 7 extract.features_report.DTM 6 0 0 41.674
## end elapsed
## 6 41.672 0.666
## 7 NA NA## Loading required package: reshape2## [1] "Reporting term weights for descr.my..."
## [1] " Full TermMatrix:"
## <<DocumentTermMatrix (documents: 1211, terms: 177)>>
## Non-/sparse entries: 2518/211829
## Sparsity : 99%
## Maximal term length: 9
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in cor(as.matrix(as.DocumentTermMatrix(terms_TDM))[glb_allobs_df
## $.src == : the standard deviation is zero## [1] " Sparse TermMatrix:"
## <<DocumentTermMatrix (documents: 1211, terms: 7)>>
## Non-/sparse entries: 697/7780
## Sparsity : 92%
## Maximal term length: 7
## Weighting : term frequency - inverse document frequency (normalized) (tf-idf)## Warning in myplot_scatter(plt_terms_df, "freq", "weight", colorcol_name =
## "in.sprs"): converting in.sprs to class:factor
## Warning in rm(full_terms_mtrx): object 'full_terms_mtrx' not found## label step_major step_minor label_minor bgn
## 7 extract.features_report.DTM 6 0 0 41.674
## 8 extract.features_bind.DTM 7 0 0 43.789
## end elapsed
## 7 43.789 2.115
## 8 NA NA## Loading required package: tidyr## [1] "Binding DTM for descr.my..."## Warning in cor(as.matrix(as.DocumentTermMatrix(terms_TDM))[glb_allobs_df
## $.src == : the standard deviation is zero
## Warning: Removed 8 rows containing missing values (geom_point).## Warning: Removed 8 rows containing missing values (geom_point).## Warning: Removed 568 rows containing missing values (geom_text).## label step_major step_minor label_minor bgn
## 8 extract.features_bind.DTM 7 0 0 43.789
## 9 extract.features_bind.DXM 8 0 0 45.525
## end elapsed
## 8 45.524 1.736
## 9 NA NA
## [1] "Binding DXM for descr.my..."## Warning in rm(log_X_df, txt_X_df): object 'log_X_df' not found
# Use model info provided in description
# mydsp_obs(list(description.contains="a[[:digit:]]"), cols=glb_dsp_cols, all=TRUE)
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "prdline.my"] <- "iPad mini"
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "color"] <- "Space Gray"
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "cellular"] <- "0"
# glb_allobs_df[glb_allobs_df$UniqueID == 12474, "carrier"] <- "None"
#
# mydsp_obs(list(description.contains="m(.{4})ll"), cols=glb_dsp_cols, all=TRUE)
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "color"] <- "Black"
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "storage"] <- "64"
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "cellular"] <- "0"
# glb_allobs_df[glb_allobs_df$UniqueID == 11360, "carrier"] <- "None"
#
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "prdline.my"] <- "iPad Air"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "storage"] <- "32"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "color"] <- "White"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "cellular"] <- "0"
# glb_allobs_df[glb_allobs_df$UniqueID == 11361, "carrier"] <- "None"
# mydsp_obs(list(description.contains="mini(?!m)"), perl=TRUE, cols="D.P.mini", all=TRUE)
# mydsp_obs(list(D.P.mini=1), cols="D.P.mini", all=TRUE)
# mydsp_obs(list(D.P.mini=1, productline="Unknown"), cols="D.P.mini", all=TRUE)
# mydsp_obs(list(description.contains="(?<![fhp])air"), perl=TRUE, all=TRUE)
# mydsp_obs(list(description.contains="air"), perl=FALSE, cols="D.P.air", all=TRUE)
# mydsp_obs(list(D.P.air=1, productline="Unknown"), cols="D.P.air", all=TRUE)
# print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.mini",
# glb_rsp_var)))
# print(glb_allobs_df[(glb_allobs_df$productline == "Unknown") &
# (glb_allobs_df$D.P.mini > 0),
# c(glb_id_var, glb_category_var, glb_dsp_cols, glb_txt_vars)])
# glb_allobs_df[(glb_allobs_df$D.P.mini == 1) & (glb_allobs_df$productline == "Unknown"),
# "prdline.my"] <- "iPad mini"
# print(mycreate_sqlxtab_df(glb_allobs_df, c("prdline.my", "productline", "D.P.air",
# glb_rsp_var)))
# print(glb_allobs_df[(glb_allobs_df$productline == "Unknown") &
# (glb_allobs_df$D.P.air > 0),
# c(glb_id_var, glb_category_var, glb_dsp_cols, glb_txt_vars)])
# #glb_allobs_df[glb_allobs_df$UniqueID == 11863, "D.P.air"] <- 0
# glb_allobs_df[(glb_allobs_df$D.P.air == 1) & (glb_allobs_df$productline == "Unknown"),
# "prdline.my"] <- "iPad Air"
# print(glb_allobs_df[(glb_allobs_df$UniqueID %in% c(11767, 11811, 12156)),
# c(glb_id_var, "sold",
# "prdline.my", "color", "condition", "cellular", "carrier", "storage"
# #, "descr.my"
# )])
# glb_allobs_df[glb_allobs_df$UniqueID == 11767, "prdline.my"] <- "iPad 2"
# glb_allobs_df[glb_allobs_df$UniqueID == 11767, "storage"] <- "32"
# glb_allobs_df[glb_allobs_df$UniqueID == 11811, "prdline.my"] <- "iPad 2"
# glb_allobs_df[glb_allobs_df$UniqueID == 12156, "prdline.my"] <- "iPad 1"
# mydsp_obs(list(prdline.my="Unknown"), all=TRUE)
# tmp_allobs_df <- glb_allobs_df[, "prdline.my", FALSE]
# names(tmp_allobs_df) <- "old.prdline.my"
# glb_allobs_df$prdline.my <-
# plyr::revalue(glb_allobs_df$prdline.my, c(
# # "iPad 1" = "iPad",
# # "iPad 2" = "iPad2+",
# "iPad 3" = "iPad 3+",
# "iPad 4" = "iPad 3+",
# "iPad 5" = "iPad 3+",
#
# "iPad Air" = "iPadAir",
# "iPad Air 2" = "iPadAir",
#
# "iPad mini" = "iPadmini",
# "iPad mini 2" = "iPadmini 2+",
# "iPad mini 3" = "iPadmini 2+",
# "iPad mini Retina" = "iPadmini 2+"
# ))
# tmp_allobs_df$prdline.my <- glb_allobs_df[, "prdline.my"]
# print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my", "old.prdline.my")))
# print(mycreate_sqlxtab_df(tmp_allobs_df, c("prdline.my")))
# print(mycreate_sqlxtab_df(subset(glb_allobs_df, color == "Unknown"),
# c("color", "D.P.black", "D.P.gold", "D.P.spacegray", "D.P.white")))
# print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.black > 0),
# c(glb_id_var, "color", "D.P.black", "sold", "prdline.my", "condition",
# "cellular", "carrier", "storage", "descr.my")])
# glb_allobs_df[glb_allobs_df$UniqueID == 12137, "color"] <- "Black"
# print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.spacegray > 0),
# c(glb_id_var, "color", "D.P.spacegray", "prdline.my", "condition",
# "cellular", "carrier", "storage", "descr.my")])
# glb_allobs_df[glb_allobs_df$UniqueID %in% c(12106), "color"] <- "Space Gray"
# print(glb_allobs_df[(glb_allobs_df$color == "Unknown") & (glb_allobs_df$D.P.white > 0),
# c(glb_id_var, "color", "D.P.white", "prdline.my", "condition",
# "cellular", "carrier", "storage", "descr.my")])
# glb_allobs_df[glb_allobs_df$UniqueID %in% c(10573, 10809, 10925, 11735), "color"] <-
# "White"
glb_allobs_df$carrier.fctr <- as.factor(glb_allobs_df$carrier)
glb_allobs_df$cellular.fctr <- as.factor(glb_allobs_df$cellular)
glb_allobs_df$color.fctr <- as.factor(glb_allobs_df$color)
# glb_allobs_df$prdline.my.fctr <- as.factor(glb_allobs_df$prdline.my)
glb_allobs_df$storage.fctr <- as.factor(glb_allobs_df$storage)
#stop(here"); sav_allobs_df <- glb_allobs_df; glb_allobs_df <- sav_allobs_df
# glb_allobs_df %>%
# unite(prdl.descr.my, c(productline, as.numeric(D.chrs.n.log > 0), sep="#"))
# unite_("prdl.descr.my", interp(~c("productline", as.numeric(D.chrs.n.log > 0), sep="#")))
glb_allobs_df$prdl.descr.my.fctr <- as.factor(paste(gsub(" ", "", glb_allobs_df$productline),
as.numeric(nchar(glb_allobs_df$description) > 0),
sep="#"))
print(table(glb_allobs_df$prdl.descr.my.fctr, glb_allobs_df$sold, useNA = "ifany"))##
## 0 1 <NA>
## Unknown#0 20 32 14
## Unknown#1 8 24 24
## iPad1#0 12 55 30
## iPad1#1 10 41 20
## iPad2#0 16 67 52
## iPad2#1 9 48 18
## iPad3#0 9 43 18
## iPad3#1 5 20 5
## iPad4#0 9 29 11
## iPad4#1 10 16 15
## iPadAir#0 7 33 22
## iPadAir#1 6 28 14
## iPadAir2#0 13 35 19
## iPadAir2#1 2 16 7
## iPadmini#0 23 59 39
## iPadmini#1 14 40 20
## iPadmini2#0 7 22 21
## iPadmini2#1 5 12 13
## iPadmini3#0 10 15 11
## iPadmini3#1 2 3 3print(mycreate_sqlxtab_df(glb_allobs_df, c("prdl.descr.my.fctr", "sold")))## prdl.descr.my.fctr sold .n
## 1 iPad2#0 1 67
## 2 iPadmini#0 1 59
## 3 iPad1#0 1 55
## 4 iPad2#0 NA 52
## 5 iPad2#1 1 48
## 6 iPad3#0 1 43
## 7 iPad1#1 1 41
## 8 iPadmini#1 1 40
## 9 iPadmini#0 NA 39
## 10 iPadAir2#0 1 35
## 11 iPadAir#0 1 33
## 12 Unknown#0 1 32
## 13 iPad1#0 NA 30
## 14 iPad4#0 1 29
## 15 iPadAir#1 1 28
## 16 Unknown#1 NA 24
## 17 Unknown#1 1 24
## 18 iPadmini#0 0 23
## 19 iPadAir#0 NA 22
## 20 iPadmini2#0 1 22
## 21 iPadmini2#0 NA 21
## 22 Unknown#0 0 20
## 23 iPad1#1 NA 20
## 24 iPad3#1 1 20
## 25 iPadmini#1 NA 20
## 26 iPadAir2#0 NA 19
## 27 iPad2#1 NA 18
## 28 iPad3#0 NA 18
## 29 iPad2#0 0 16
## 30 iPad4#1 1 16
## 31 iPadAir2#1 1 16
## 32 iPad4#1 NA 15
## 33 iPadmini3#0 1 15
## 34 Unknown#0 NA 14
## 35 iPadAir#1 NA 14
## 36 iPadmini#1 0 14
## 37 iPadAir2#0 0 13
## 38 iPadmini2#1 NA 13
## 39 iPad1#0 0 12
## 40 iPadmini2#1 1 12
## 41 iPad4#0 NA 11
## 42 iPadmini3#0 NA 11
## 43 iPad1#1 0 10
## 44 iPad4#1 0 10
## 45 iPadmini3#0 0 10
## 46 iPad2#1 0 9
## 47 iPad3#0 0 9
## 48 iPad4#0 0 9
## 49 Unknown#1 0 8
## 50 iPadAir#0 0 7
## 51 iPadAir2#1 NA 7
## 52 iPadmini2#0 0 7
## 53 iPadAir#1 0 6
## 54 iPad3#1 NA 5
## 55 iPad3#1 0 5
## 56 iPadmini2#1 0 5
## 57 iPadmini3#1 NA 3
## 58 iPadmini3#1 1 3
## 59 iPadAir2#1 0 2
## 60 iPadmini3#1 0 2glb_category_var <- "prdl.descr.my.fctr"
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# print(myplot_scatter(glb_trnobs_df, "<col1_name>", "<col2_name>", smooth=TRUE))
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
if (!is.null(glb_price_vars)) {
for (var in glb_price_vars) {
for (digit in 1:(log10(max(glb_allobs_df[, var], na.rm=TRUE)) + 1)) {
glb_allobs_df[, paste0(var, ".dgt", digit, ".is9")] <-
as.numeric(as.integer((as.integer(glb_allobs_df[, var]) %% (10 ^ digit)) /
(10 ^ (digit - 1))) == 9)
}
for (decimal in 1:2) {
glb_allobs_df[, paste0(var, ".dcm", decimal, ".is9")] <-
as.numeric(as.integer(glb_allobs_df[, var] * (10 ^ decimal)) %% 10 == 9)
}
}
#as.numeric((as.integer(startprice) %% 10) == 9)
}
rm(corpus_lst
, glb_sprs_DTM_lst #, glb_full_DTM_lst
, txt_corpus, txt_vctr)## Warning in rm(corpus_lst, glb_sprs_DTM_lst, txt_corpus, txt_vctr): object
## 'corpus_lst' not foundextract.features_chunk_df <- myadd_chunk(extract.features_chunk_df, "extract.features_end",
major.inc=TRUE)## label step_major step_minor label_minor bgn
## 9 extract.features_bind.DXM 8 0 0 45.525
## 10 extract.features_end 9 0 0 60.283
## end elapsed
## 9 60.28 14.756
## 10 NA NAmyplt_chunk(extract.features_chunk_df)## label step_major
## 9 extract.features_bind.DXM 8
## 5 extract.features_build.corpus 4
## 7 extract.features_report.DTM 6
## 4 extract.features_process.text_reporting_compound_terms 3
## 8 extract.features_bind.DTM 7
## 3 extract.features_process.text 3
## 6 extract.features_extract.DTM 5
## 2 extract.features_factorize.str.vars 2
## 1 extract.features_bgn 1
## step_minor label_minor bgn end elapsed duration
## 9 0 0 45.525 60.280 14.756 14.755
## 5 0 0 35.429 41.007 5.578 5.578
## 7 0 0 41.674 43.789 2.115 2.115
## 4 1 1 33.330 35.428 2.099 2.098
## 8 0 0 43.789 45.524 1.736 1.735
## 3 0 0 32.496 33.330 0.834 0.834
## 6 0 0 41.007 41.672 0.666 0.665
## 2 0 0 32.435 32.495 0.060 0.060
## 1 0 0 32.419 32.435 0.016 0.016
## [1] "Total Elapsed Time: 60.28 secs"
# if (glb_save_envir)
# save(glb_feats_df,
# glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
# file=paste0(glb_out_pfx, "extract_features_dsk.RData"))
# load(paste0(glb_out_pfx, "extract_features_dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all","data.new")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "cluster.data", major.inc=TRUE)## label step_major step_minor label_minor bgn end elapsed
## 5 extract.features 3 0 0 32.411 61.918 29.507
## 6 cluster.data 4 0 0 61.919 NA NAStep 4.0: cluster data
glb_chunks_df <- myadd_chunk(glb_chunks_df, "manage.missing.data", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 6 cluster.data 4 0 0 61.919 63.86
## 7 manage.missing.data 4 1 1 63.861 NA
## elapsed
## 6 1.941
## 7 NA# If mice crashes with error: Error in get(as.character(FUN), mode = "function", envir = envir) : object 'State' of mode 'function' was not found
# consider excluding 'State' as a feature
# print(sapply(names(glb_trnobs_df), function(col) sum(is.na(glb_trnobs_df[, col]))))
# print(sapply(names(glb_newobs_df), function(col) sum(is.na(glb_newobs_df[, col]))))
# glb_trnobs_df <- na.omit(glb_trnobs_df)
# glb_newobs_df <- na.omit(glb_newobs_df)
# df[is.na(df)] <- 0
mycheck_problem_data(glb_allobs_df)## [1] "numeric data missing in : "
## sold
## 376
## [1] "numeric data w/ 0s in : "
## sold startprice.log10
## 197 31
## cellular.fctr D.terms.post.stop.n
## 789 754
## D.terms.post.stop.n.log D.weight.post.stop.sum
## 754 754
## D.terms.post.stem.n D.terms.post.stem.n.log
## 754 754
## D.weight.post.stem.sum D.T.condit
## 754 1049
## D.T.use D.T.new
## 1082 1157
## D.T.screen D.T.scratch
## 1125 1106
## D.T.good D.T.great
## 1145 1157
## D.T.excel D.T.work
## 1164 1138
## D.T.no D.T.function.
## 1135 1155
## D.T.like D.T.crack
## 1186 1174
## D.T.fulli D.T.box
## 1175 1177
## D.T.mint D.T.item
## 1192 1167
## D.T.lock D.T.see
## 1186 1181
## D.T.icloud D.T.perfect
## 1191 1193
## D.wrds.n.log D.wrds.unq.n.log
## 753 754
## D.weight.sum D.ratio.weight.sum.wrds.n
## 754 754
## D.chrs.n.log D.chrs.uppr.n.log
## 753 753
## D.dgts.n.log D.chrs.pnct01.n.log
## 1139 1173
## D.chrs.pnct02.n.log D.chrs.pnct03.n.log
## 1211 1188
## D.chrs.pnct04.n.log D.chrs.pnct05.n.log
## 1211 1204
## D.chrs.pnct06.n.log D.chrs.pnct07.n.log
## 1178 1210
## D.chrs.pnct08.n.log D.chrs.pnct09.n.log
## 1194 1210
## D.chrs.pnct10.n.log D.chrs.pnct11.n.log
## 1211 1059
## D.chrs.pnct12.n.log D.chrs.pnct13.n.log
## 1165 919
## D.chrs.pnct14.n.log D.chrs.pnct15.n.log
## 1191 1205
## D.chrs.pnct16.n.log D.chrs.pnct17.n.log
## 1175 1211
## D.chrs.pnct18.n.log D.chrs.pnct19.n.log
## 1211 1211
## D.chrs.pnct20.n.log D.chrs.pnct21.n.log
## 1211 1211
## D.chrs.pnct22.n.log D.chrs.pnct23.n.log
## 1211 1211
## D.chrs.pnct24.n.log D.chrs.pnct25.n.log
## 1211 1211
## D.chrs.pnct26.n.log D.chrs.pnct27.n.log
## 1211 1211
## D.chrs.pnct28.n.log D.chrs.pnct29.n.log
## 1211 1211
## D.chrs.pnct30.n.log D.wrds.stop.n.log
## 1211 822
## D.P.http D.P.mini
## 1211 1197
## D.P.air D.P.black
## 1202 1199
## D.P.white D.P.gold
## 1206 1211
## D.P.spacegray startprice.dgt1.is9
## 1207 908
## startprice.dgt2.is9 startprice.dgt3.is9
## 1064 1211
## startprice.dcm1.is9 startprice.dcm2.is9
## 775 830
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 753 0 0 0 0 0
## productline .grpid descr.my
## 0 NA 753# glb_allobs_df <- na.omit(glb_allobs_df)
# Not refactored into mydsutils.R since glb_*_df might be reassigned
glb_impute_missing_data <- function() {
require(mice)
set.seed(glb_mice_complete.seed)
inp_impent_df <- glb_allobs_df[, setdiff(names(glb_allobs_df),
union(glb_exclude_vars_as_features, glb_rsp_var))]
print("Summary before imputation: ")
print(summary(inp_impent_df))
out_impent_df <- complete(mice(inp_impent_df))
print(summary(out_impent_df))
ret_vars <- sapply(names(out_impent_df),
function(col) ifelse(!identical(out_impent_df[, col],
inp_impent_df[, col]),
col, ""))
ret_vars <- ret_vars[ret_vars != ""]
# complete(mice()) changes attributes of factors even though values don't change
for (col in ret_vars) {
if (inherits(out_impent_df[, col], "factor")) {
if (identical(as.numeric(out_impent_df[, col]),
as.numeric(inp_impent_df[, col])))
ret_vars <- setdiff(ret_vars, col)
}
}
return(out_impent_df[, ret_vars])
}
if (glb_impute_na_data &&
(length(myfind_numerics_missing(glb_allobs_df)) > 0) &&
(ncol(nonna_df <- glb_impute_missing_data()) > 0)) {
for (col in names(nonna_df)) {
glb_allobs_df[, paste0(col, ".nonNA")] <- nonna_df[, col]
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, col)
}
}
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold
## 376
## [1] "numeric data w/ 0s in : "
## sold startprice.log10
## 197 31
## cellular.fctr D.terms.post.stop.n
## 789 754
## D.terms.post.stop.n.log D.weight.post.stop.sum
## 754 754
## D.terms.post.stem.n D.terms.post.stem.n.log
## 754 754
## D.weight.post.stem.sum D.T.condit
## 754 1049
## D.T.use D.T.new
## 1082 1157
## D.T.screen D.T.scratch
## 1125 1106
## D.T.good D.T.great
## 1145 1157
## D.T.excel D.T.work
## 1164 1138
## D.T.no D.T.function.
## 1135 1155
## D.T.like D.T.crack
## 1186 1174
## D.T.fulli D.T.box
## 1175 1177
## D.T.mint D.T.item
## 1192 1167
## D.T.lock D.T.see
## 1186 1181
## D.T.icloud D.T.perfect
## 1191 1193
## D.wrds.n.log D.wrds.unq.n.log
## 753 754
## D.weight.sum D.ratio.weight.sum.wrds.n
## 754 754
## D.chrs.n.log D.chrs.uppr.n.log
## 753 753
## D.dgts.n.log D.chrs.pnct01.n.log
## 1139 1173
## D.chrs.pnct02.n.log D.chrs.pnct03.n.log
## 1211 1188
## D.chrs.pnct04.n.log D.chrs.pnct05.n.log
## 1211 1204
## D.chrs.pnct06.n.log D.chrs.pnct07.n.log
## 1178 1210
## D.chrs.pnct08.n.log D.chrs.pnct09.n.log
## 1194 1210
## D.chrs.pnct10.n.log D.chrs.pnct11.n.log
## 1211 1059
## D.chrs.pnct12.n.log D.chrs.pnct13.n.log
## 1165 919
## D.chrs.pnct14.n.log D.chrs.pnct15.n.log
## 1191 1205
## D.chrs.pnct16.n.log D.chrs.pnct17.n.log
## 1175 1211
## D.chrs.pnct18.n.log D.chrs.pnct19.n.log
## 1211 1211
## D.chrs.pnct20.n.log D.chrs.pnct21.n.log
## 1211 1211
## D.chrs.pnct22.n.log D.chrs.pnct23.n.log
## 1211 1211
## D.chrs.pnct24.n.log D.chrs.pnct25.n.log
## 1211 1211
## D.chrs.pnct26.n.log D.chrs.pnct27.n.log
## 1211 1211
## D.chrs.pnct28.n.log D.chrs.pnct29.n.log
## 1211 1211
## D.chrs.pnct30.n.log D.wrds.stop.n.log
## 1211 822
## D.P.http D.P.mini
## 1211 1197
## D.P.air D.P.black
## 1202 1199
## D.P.white D.P.gold
## 1206 1211
## D.P.spacegray startprice.dgt1.is9
## 1207 908
## startprice.dgt2.is9 startprice.dgt3.is9
## 1064 1211
## startprice.dcm1.is9 startprice.dcm2.is9
## 775 830
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 753 0 0 0 0 0
## productline .grpid descr.my
## 0 NA 753Step 4.1: manage missing data
mycompute_entropy_df <- function(obs_df, entropy_var, by_var=NULL) {
require(lazyeval)
require(dplyr)
require(tidyr)
if (is.null(by_var)) {
by_var <- ".default"
obs_df$.default <- as.factor(".default")
}
if (!any(grepl(".clusterid", names(obs_df), fixed=TRUE)))
obs_df$.clusterid <- 1
cluster_df <- obs_df %>%
count_(c(by_var, ".clusterid", entropy_var)) %>%
dplyr::filter(n > 0) %>%
dplyr::filter_(interp(~(!is.na(var)), var=as.name(entropy_var))) %>%
unite_(paste0(by_var, ".clusterid"),
c(interp(by_var), ".clusterid")) %>%
spread_(interp(entropy_var), "n", fill=0)
# head(cluster_df)
# sum(cluster_df$n)
tmp.entropy <- sapply(1:nrow(cluster_df),
function(row) entropy(as.numeric(cluster_df[row, -1]), method="ML"))
tmp.knt <- sapply(1:nrow(cluster_df),
function(row) sum(as.numeric(cluster_df[row, -1])))
cluster_df$.entropy <- tmp.entropy; cluster_df$.knt <- tmp.knt
#print(cluster_df)
return(cluster_df)
}
if (glb_cluster) {
require(proxy)
#require(hash)
require(dynamicTreeCut)
require(entropy)
require(tidyr)
mywgtdcosine_dist <- function(x, y=NULL, weights=NULL) {
if (!inherits(x, "matrix"))
x <- as.matrix(x)
if (is.null(weights))
weights <- rep(1, ncol(x))
wgtsx <- matrix(rep(weights / sum(weights), nrow(x)), nrow=nrow(x), byrow=TRUE)
wgtdx <- x * wgtsx
wgtdxsqsum <- as.matrix(rowSums((x ^ 2) * wgtsx), byrow=FALSE)
denom <- sqrt(wgtdxsqsum %*% t(wgtdxsqsum))
ret_mtrx <- 1 - ((sum(weights) ^ 1) * (wgtdx %*% t(wgtdx)) / denom)
ret_mtrx[is.nan(ret_mtrx)] <- 1
diag(ret_mtrx) <- 0
return(ret_mtrx)
}
#pr_DB$delete_entry("mywgtdcosine");
# Need to do this only once across runs ?
if (!pr_DB$entry_exists("mywgtdcosine")) {
pr_DB$set_entry(FUN = mywgtdcosine_dist, names = c("mywgtdcosine"))
pr_DB$modify_entry(names="mywgtdcosine", type="metric", loop=FALSE)
}
#pr_DB$get_entry("mywgtdcosine")
# glb_hash <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
# glb_hash_lst <- hash(key=unique(glb_allobs_df$myCategory),
# values=1:length(unique(glb_allobs_df$myCategory)))
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
cluster_vars <- grep(paste0("[",
toupper(paste0(substr(glb_txt_vars, 1, 1), collapse="")),
"]\\.[PT]\\."),
names(glb_allobs_df), value=TRUE)
# Assign correlations with rsp_var as weights for cosine distance
print("Clustering features: ")
cluster_vars_df <- data.frame(abs.cor.y=abs(cor(
glb_allobs_df[glb_allobs_df$.src == "Train", cluster_vars],
glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var],
use="pairwise.complete.obs")))
print(tail(cluster_vars_df <- orderBy(~ abs.cor.y, subset(cluster_vars_df, !is.na(abs.cor.y))), 5))
print(sprintf(" .rnorm cor: %0.4f",
cor(glb_allobs_df[glb_allobs_df$.src == "Train", ".rnorm"],
glb_allobs_df[glb_allobs_df$.src == "Train", glb_rsp_var], use="pairwise.complete.obs")))
print(sprintf("glb_allobs_df Entropy: %0.4f",
allobs_ent <- entropy(table(glb_allobs_df[, glb_cluster_entropy_var]),
method="ML")))
print(category_df <- mycompute_entropy_df(obs_df=glb_allobs_df,
entropy_var=glb_cluster_entropy_var,
by_var=glb_category_var))
print(sprintf("glb_allobs_df$%s Entropy: %0.4f (%0.4f pct)",
glb_category_var,
category_ent <- weighted.mean(category_df$.entropy, category_df$.knt),
100 * category_ent / allobs_ent))
glb_allobs_df$.clusterid <- 1
#print(max(table(glb_allobs_df$myCategory.fctr) / 20))
grp_ids <- sort(unique(glb_allobs_df[, glb_category_var]))
glb_cluster_size_df_lst <- list()
for (grp in grp_ids) {
# if (grep(grp, levels(grp_ids)) <= 6) next
# if (grep(grp, levels(grp_ids)) > 9) next
# if (grep(grp, levels(grp_ids)) != 10) next
print(sprintf("Category: %s", grp))
ctgry_allobs_df <- glb_allobs_df[glb_allobs_df[, glb_category_var] == grp, ]
if (!inherits(ctgry_allobs_df[, glb_cluster_entropy_var], "factor"))
ctgry_allobs_df[, glb_cluster_entropy_var] <-
as.factor(ctgry_allobs_df[, glb_cluster_entropy_var])
#dstns_dist <- proxy::dist(ctgry_allobs_df[, cluster_vars], method = "cosine")
dstns_dist <- proxy::dist(ctgry_allobs_df[, row.names(cluster_vars_df)],
method = "mywgtdcosine",
weights=cluster_vars_df$abs.cor.y)
# Custom distance functions return a crossdist object
#dstns_mtrx <- as.matrix(dstns_dist)
dstns_mtrx <- matrix(as.vector(dstns_dist), nrow=attr(dstns_dist, "dim")[1],
dimnames=attr(dstns_dist, "dimnames"))
dstns_dist <- as.dist(dstns_mtrx)
print(sprintf("max distance(%0.4f) pair:", max(dstns_mtrx)))
# print(dim(dstns_mtrx))
# print(sprintf("which.max: %d", which.max(dstns_mtrx)))
row_ix <- ceiling(which.max(dstns_mtrx) / ncol(dstns_mtrx))
col_ix <- which.max(dstns_mtrx[row_ix, ])
# print(sprintf("row_ix: %d", row_ix)); print(sprintf("col_ix: %d", col_ix));
# print(dim(ctgry_allobs_df))
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_cluster_entropy_var, glb_category_var, glb_txt_vars, cluster_vars)])
min_dstns_mtrx <- dstns_mtrx
diag(min_dstns_mtrx) <- 1
# Float representations issue -2.22e-16 vs. 0.0000
print(sprintf("min distance(%0.4f) pair:", min(min_dstns_mtrx)))
row_ix <- ceiling(which.min(min_dstns_mtrx) / ncol(min_dstns_mtrx))
col_ix <- which.min(min_dstns_mtrx[row_ix, ])
print(ctgry_allobs_df[c(row_ix, col_ix),
c(glb_id_var, glb_cluster_entropy_var, glb_category_var, glb_txt_vars,
cluster_vars)])
set.seed(glb_cluster.seed)
clusters <- hclust(dstns_dist, method = "ward.D2")
# Workaround to avoid "Error in cutree(dendro, h = heightcutoff) : the 'height' component of 'tree' is not sorted (increasingly)"
if (with(clusters,all.equal(height,sort(height))))
clusters$height <- round(clusters$height,6)
myplclust(clusters, lab=ctgry_allobs_df[, glb_id_var],
lab.col=unclass(ctgry_allobs_df[, glb_cluster_entropy_var]))
opt_minclustersize_df <- data.frame(minclustersize=nrow(ctgry_allobs_df),
entropy=entropy(table(ctgry_allobs_df[, glb_cluster_entropy_var]),
method="ML"))
for (minclustersize in
as.integer(seq(nrow(ctgry_allobs_df) / 2, nrow(ctgry_allobs_df) / 10, length=5))) {
clusterGroups <- cutreeDynamic(clusters, minClusterSize=minclustersize, method="tree",
deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
clusterGroups[clusterGroups == 0] <- 1
ctgry_allobs_df$.clusterid <- clusterGroups
ctgry_clstrs_df <- mycompute_entropy_df(ctgry_allobs_df, glb_cluster_entropy_var)
opt_minclustersize_df <- rbind(opt_minclustersize_df,
data.frame(minclustersize=minclustersize,
entropy=weighted.mean(ctgry_clstrs_df$.entropy, ctgry_clstrs_df$.knt)))
}
opt_minclustersize <-
opt_minclustersize_df$minclustersize[which.min(opt_minclustersize_df$entropy)]
opt_minclustersize_df$.color <-
ifelse(opt_minclustersize_df$minclustersize == opt_minclustersize,
"red", "blue")
print(ggplot(data=opt_minclustersize_df, mapping=aes(x=minclustersize, y=entropy)) +
geom_point(aes(color=.color)) + scale_color_identity() + guides(color = "none") +
geom_line())
glb_cluster_size_df_lst[[grp]] <- opt_minclustersize_df
# select minclustersize that minimizes entropy
clusterGroups <- cutreeDynamic(clusters, minClusterSize=opt_minclustersize,
method="tree",
deepSplit=0)
# Unassigned groups are labeled 0; the largest group has label 1
table(clusterGroups, ctgry_allobs_df[, glb_cluster_entropy_var], useNA="ifany")
clusterGroups[clusterGroups == 0] <- 1
table(clusterGroups, ctgry_allobs_df[, glb_cluster_entropy_var], useNA="ifany")
glb_allobs_df[glb_allobs_df[, glb_category_var]==grp,]$.clusterid <- clusterGroups
}
#all.equal(sav_allobs_df_clusterid, glb_allobs_df$.clusterid)
print(cluster_df <- mycompute_entropy_df(obs_df=glb_allobs_df,
entropy_var=glb_cluster_entropy_var,
by_var=glb_category_var))
print(sprintf("glb_allobs_df$%s$.clusterid Entropy: %0.4f (%0.4f pct)",
glb_category_var,
cluster_ent <- weighted.mean(cluster_df$.entropy, cluster_df$.knt),
100 * cluster_ent / category_ent))
glb_allobs_df$.clusterid.fctr <- as.factor(glb_allobs_df$.clusterid)
# .clusterid.fctr is created automatically (probably ?) later
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features, ".clusterid")
if (!is.null(glb_category_var))
# glb_interaction_only_feats_lst[ifelse(grepl("\\.fctr", glb_category_var),
# glb_category_var,
# paste0(glb_category_var, ".fctr"))] <-
# c(".clusterid.fctr")
glb_interaction_only_feats_lst[[".clusterid.fctr"]] <-
ifelse(grepl("\\.fctr", glb_category_var), glb_category_var,
paste0(glb_category_var, ".fctr"))
if (glb_exclude_cluster_vars_as_features)
glb_exclude_vars_as_features <- c(glb_exclude_vars_as_features,
cluster_vars)
}## Loading required package: proxy
##
## Attaching package: 'proxy'
##
## The following objects are masked from 'package:stats':
##
## as.dist, dist
##
## The following object is masked from 'package:base':
##
## as.matrix
##
## Loading required package: dynamicTreeCut
## Loading required package: entropy## [1] "Clustering features: "## Warning in cor(glb_allobs_df[glb_allobs_df$.src == "Train",
## cluster_vars], : the standard deviation is zero## abs.cor.y
## D.T.good 0.09655397
## D.T.screen 0.10684460
## D.T.box 0.10867137
## D.T.fulli 0.18029463
## D.T.function. 0.22075684
## [1] " .rnorm cor: 0.0291"
## [1] "glb_allobs_df Entropy: 0.5463"## Loading required package: lazyeval## Source: local data frame [20 x 5]
##
## prdl.descr.my.fctr.clusterid 0 1 .entropy .knt
## (chr) (dbl) (dbl) (dbl) (dbl)
## 1 Unknown#0_1 20 32 0.6662784 52
## 2 Unknown#1_1 8 24 0.5623351 32
## 3 iPad1#0_1 12 55 0.4700328 67
## 4 iPad1#1_1 10 41 0.4949177 51
## 5 iPad2#0_1 16 67 0.4902162 83
## 6 iPad2#1_1 9 48 0.4361623 57
## 7 iPad3#0_1 9 43 0.4607317 52
## 8 iPad3#1_1 5 20 0.5004024 25
## 9 iPad4#0_1 9 29 0.5474125 38
## 10 iPad4#1_1 10 16 0.6662784 26
## 11 iPadAir#0_1 7 33 0.4637264 40
## 12 iPadAir#1_1 6 28 0.4659993 34
## 13 iPadAir2#0_1 13 35 0.5840859 48
## 14 iPadAir2#1_1 2 16 0.3488321 18
## 15 iPadmini#0_1 23 59 0.5934134 82
## 16 iPadmini#1_1 14 40 0.5722807 54
## 17 iPadmini2#0_1 7 22 0.5526646 29
## 18 iPadmini2#1_1 5 12 0.6057975 17
## 19 iPadmini3#0_1 10 15 0.6730117 25
## 20 iPadmini3#1_1 2 3 0.6730117 5
## [1] "glb_allobs_df$prdl.descr.my.fctr Entropy: 0.5321 (97.3935 pct)"
## [1] "Category: Unknown#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 47 10145 1 Unknown#0 0 0 0
## 85 10211 1 Unknown#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 47 0 0 0 0 0 0 0
## 85 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 47 0 0 0 0 0 0 0
## 85 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 47 0 0 0 0 0 0 0
## 85 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 47 0 0 0 0
## 85 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 47 10145 1 Unknown#0 0 0 0
## 47.1 10145 1 Unknown#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 47 0 0 0 0 0 0 0
## 47.1 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 47 0 0 0 0 0 0 0
## 47.1 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 47 0 0 0 0 0 0 0
## 47.1 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 47 0 0 0 0
## 47.1 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: Unknown#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 38 10130 1 Unknown#1
## 79 10201 1 Unknown#1
## descr.my D.T.condit D.T.use
## 38 New - Open Box. Charger included. 0 0
## 79 Screen are in great shape! Digitizer issues. 0 0
## D.T.new D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work
## 38 0.8974191 0.000000 0 0 0.000000 0 0
## 79 0.0000000 1.271906 0 0 1.495699 0 0
## D.T.no D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint
## 38 0 0 0 0 0 1.030904 0
## 79 0 0 0 0 0 0.000000 0
## D.T.item D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini
## 38 0 0 0 0 0 0 0
## 79 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 38 0 0 0 0 0
## 79 0 0 0 0 0
## [1] "min distance(0.8322) pair:"
## UniqueID sold prdl.descr.my.fctr
## 804 11729 1 Unknown#1
## 1012 12156 <NA> Unknown#1
## descr.my
## 804 Cracked Screen, but device is still fully functional. iCloud Unlocked.
## 1012 Original IPAD 1st generation - used one owner (myself)Good shape as pictures. Fully functional as
## D.T.condit D.T.use D.T.new D.T.screen D.T.scratch D.T.good
## 804 0 0.0000000 0 0.5451026 0 0
## 1012 0 0.4615366 0 0.0000000 0 0
## D.T.great D.T.excel D.T.work D.T.no D.T.function. D.T.like D.T.crack
## 804 0 0 0 0 0.6335183 0 0.7189328
## 1012 0 0 0 0 0.6335183 0 0.0000000
## D.T.fulli D.T.box D.T.mint D.T.item D.T.lock D.T.see D.T.icloud
## 804 0.7245797 0 0 0 0 0 0.8457222
## 1012 0.7245797 0 0 0 0 0 0.0000000
## D.T.perfect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 804 0 0 0 0 0 0 0
## 1012 0 0 0 0 0 0 0
## D.P.spacegray
## 804 0
## 1012 0
## [1] "Category: iPad1#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 4 10009 1 iPad1#0 0 0 0
## 16 10074 1 iPad1#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 4 0 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 4 0 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 4 0 0 0 0 0 0 0
## 16 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 4 0 0 0 0
## 16 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 4 10009 1 iPad1#0 0 0 0
## 4.1 10009 1 iPad1#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 4 0 0 0 0 0 0 0
## 4.1 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 4 0 0 0 0 0 0 0
## 4.1 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 4 0 0 0 0 0 0 0
## 4.1 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 4 0 0 0 0
## 4.1 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPad1#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 6 10013 1 iPad1#1
## 92 10226 1 iPad1#1
## descr.my
## 6 GOOD CONDITION. CLEAN ICLOUD. NO locked. CLEAN IMEI. This tablet has been fully tested and works
## 92 I replaced the battery in this IPad roughly one year ago. I was unable to properly reassemble it
## D.T.condit D.T.use D.T.new D.T.screen D.T.scratch D.T.good D.T.great
## 6 0.2902133 0 0 0 0 0.4197589 0
## 92 0.0000000 0 0 0 0 0.0000000 0
## D.T.excel D.T.work D.T.no D.T.function. D.T.like D.T.crack
## 6 0 0.4052159 0.3994056 0 0 0
## 92 0 0.0000000 0.0000000 0 0 0
## D.T.fulli D.T.box D.T.mint D.T.item D.T.lock D.T.see D.T.icloud
## 6 0.5072058 0 0 0 0.5598127 0 0.5920055
## 92 0.0000000 0 0 0 0.0000000 0 0.0000000
## D.T.perfect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 6 0 0 0 0 0 0 0
## 92 0 0 0 0 0 0 0
## D.P.spacegray
## 6 0
## 92 0
## [1] "min distance(0.8001) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use
## 173 10340 1 iPad1#1 FULLY FUNCTIONAL 0 0
## 570 10998 1 iPad1#1 FULLY FUNCTIONAL 0 0
## D.T.new D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work
## 173 0 0 0 0 0 0 0
## 570 0 0 0 0 0 0 0
## D.T.no D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint
## 173 0 2.217314 0 0 2.536029 0 0
## 570 0 2.217314 0 0 2.536029 0 0
## D.T.item D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini
## 173 0 0 0 0 0 0 0
## 570 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 173 0 0 0 0 0
## 570 0 0 0 0 0
## [1] "Category: iPad2#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 15 10073 1 iPad2#0 0 0 0
## 19 10081 1 iPad2#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 15 0 0 0 0 0 0 0
## 19 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 15 0 0 0 0 0 0 0
## 19 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 15 0 0 0 0 0 0 0
## 19 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 15 0 0 0 0
## 19 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 15 10073 1 iPad2#0 0 0 0
## 15.1 10073 1 iPad2#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 15 0 0 0 0 0 0 0
## 15.1 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 15 0 0 0 0 0 0 0
## 15.1 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 15 0 0 0 0 0 0 0
## 15.1 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 15 0 0 0 0
## 15.1 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPad2#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 1 10002 1 iPad2#1
## 49 10151 1 iPad2#1
## descr.my
## 1 used, please read description. May show signs of use such as scratches to the screen and
## 49 This iPad 2 is iCloud locked I repeat it is iCloud locked if you can get the iCloud locked off of it
## D.T.condit D.T.use D.T.new D.T.screen D.T.scratch D.T.good D.T.great
## 1 0 0.7179458 0 0.4239687 0.3919708 0 0
## 49 0 0.0000000 0 0.0000000 0.0000000 0 0
## D.T.excel D.T.work D.T.no D.T.function. D.T.like D.T.crack D.T.fulli
## 1 0 0 0 0 0 0 0
## 49 0 0 0 0 0 0 0
## D.T.box D.T.mint D.T.item D.T.lock D.T.see D.T.icloud D.T.perfect
## 1 0 0 0 0.000000 0 0.000000 0
## 49 0 0 0 2.799063 0 2.960028 0
## D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 1 0 0 0 0 0 0 0
## 49 0 0 0 0 0 0 0
## [1] "min distance(0.8203) pair:"
## UniqueID sold prdl.descr.my.fctr
## 774 11681 1 iPad2#1
## 1124 12392 <NA> iPad2#1
## descr.my
## 774 Non- functional Unit, handset only. Please refer to the item description below. Contact us with any
## 1124 100% functional, no damage.
## D.T.condit D.T.use D.T.new D.T.screen D.T.scratch D.T.good D.T.great
## 774 0 0 0 0 0 0 0
## 1124 0 0 0 0 0 0 0
## D.T.excel D.T.work D.T.no D.T.function. D.T.like D.T.crack
## 774 0 0 0.0000000 0.4434628 0 0
## 1124 0 0 0.9985139 1.1086571 0 0
## D.T.fulli D.T.box D.T.mint D.T.item D.T.lock D.T.see D.T.icloud
## 774 0 0 0 0.4782552 0 0 0
## 1124 0 0 0 0.0000000 0 0 0
## D.T.perfect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 774 0 0 0 0 0 0 0
## 1124 0 0 0 0 0 0 0
## D.P.spacegray
## 774 0
## 1124 0
## [1] "Category: iPad3#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 24 10097 1 iPad3#0 0 0 0
## 35 10124 1 iPad3#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 24 0 0 0 0 0 0 0
## 35 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 24 0 0 0 0 0 0 0
## 35 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 24 0 0 0 0 0 0 0
## 35 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 24 0 0 0 0
## 35 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 24 10097 1 iPad3#0 0 0 0
## 24.1 10097 1 iPad3#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 24 0 0 0 0 0 0 0
## 24.1 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 24 0 0 0 0 0 0 0
## 24.1 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 24 0 0 0 0 0 0 0
## 24.1 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 24 0 0 0 0
## 24.1 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPad3#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 14 10070 1 iPad3#1
## 50 10152 1 iPad3#1
## descr.my
## 14 Heavily used, digitizer is cracked in top right corner, plastic cover over 4G antenna has a crack in
## 50 Includes iPad, charger, wall plug, and Apple iPad Smart Case. Front screen excellent condition
## D.T.condit D.T.use D.T.new D.T.screen D.T.scratch D.T.good D.T.great
## 14 0.0000000 0.4615366 0 0.0000000 0 0 0
## 50 0.3224592 0.0000000 0 0.4239687 0 0 0
## D.T.excel D.T.work D.T.no D.T.function. D.T.like D.T.crack D.T.fulli
## 14 0.0000000 0 0 0 0 1.437866 0
## 50 0.5208216 0 0 0 0 0.000000 0
## D.T.box D.T.mint D.T.item D.T.lock D.T.see D.T.icloud D.T.perfect
## 14 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0
## D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 14 0 0 0 0 0 0 0
## 50 0 0 0 0 0 0 0
## [1] "min distance(0.8131) pair:"
## UniqueID sold prdl.descr.my.fctr
## 1031 12186 <NA> iPad3#1
## 1061 12231 <NA> iPad3#1
## descr.my
## 1031 Fully functional, in excellent condition. All original paperwork and packaging is included.
## 1061 iPad is fully functional. Some dings and scratches on the body I've tried to make clear in the
## D.T.condit D.T.use D.T.new D.T.screen D.T.scratch D.T.good D.T.great
## 1031 0.4145904 0 0 0 0.0000000 0 0
## 1061 0.0000000 0 0 0 0.8819344 0 0
## D.T.excel D.T.work D.T.no D.T.function. D.T.like D.T.crack D.T.fulli
## 1031 0.6696278 0 0 0.6335183 0 0 0.7245797
## 1061 0.0000000 0 0 1.1086571 0 0 1.2680145
## D.T.box D.T.mint D.T.item D.T.lock D.T.see D.T.icloud D.T.perfect
## 1031 0 0 0 0 0 0 0
## 1061 0 0 0 0 0 0 0
## D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 1031 0 0 0 0 0 0 0
## 1061 0 0 0 0 0 0 0
## [1] "Category: iPad4#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 5 10010 1 iPad4#0 0 0 0
## 36 10125 1 iPad4#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 5 0 0 0 0 0 0 0
## 36 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 5 0 0 0 0 0 0 0
## 36 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 5 0 0 0 0 0 0 0
## 36 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 5 0 0 0 0
## 36 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 5 10010 1 iPad4#0 0 0 0
## 5.1 10010 1 iPad4#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 5 0 0 0 0 0 0 0
## 5.1 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 5 0 0 0 0 0 0 0
## 5.1 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 5 0 0 0 0 0 0 0
## 5.1 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 5 0 0 0 0
## 5.1 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPad4#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use
## 8 10049 1 iPad4#1 great shape 0 0
## 128 10281 1 iPad4#1 Adult owned 0 0
## D.T.new D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work
## 8 0 0 0 0 4.487096 0 0
## 128 0 0 0 0 0.000000 0 0
## D.T.no D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint
## 8 0 0 0 0 0 0 0
## 128 0 0 0 0 0 0 0
## D.T.item D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini
## 8 0 0 0 0 0 0 0
## 128 0 0 0 0 0 0 0
## D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 8 0 0 0 0 0
## 128 0 0 0 0 0
## [1] "min distance(0.8256) pair:"
## UniqueID sold prdl.descr.my.fctr
## 780 11690 1 iPad4#1
## 782 11693 0 iPad4#1
## descr.my
## 780 Non- functional Unit, handset only. Please refer to the item description below. Contact us with any
## 782 This item is broken and does not function. It is being sold for parts only. See pictures for
## D.T.condit D.T.use D.T.new D.T.screen D.T.scratch D.T.good D.T.great
## 780 0 0 0 0 0 0 0
## 782 0 0 0 0 0 0 0
## D.T.excel D.T.work D.T.no D.T.function. D.T.like D.T.crack D.T.fulli
## 780 0 0 0 0.4434628 0 0 0
## 782 0 0 0 0.7391047 0 0 0
## D.T.box D.T.mint D.T.item D.T.lock D.T.see D.T.icloud D.T.perfect
## 780 0 0 0.4782552 0 0.0000000 0 0
## 782 0 0 0.7970919 0 0.8891821 0 0
## D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 780 0 0 0 0 0 0 0
## 782 0 0 0 0 0 0 0
## [1] "Category: iPadAir#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 7 10034 1 iPadAir#0 0 0 0
## 9 10050 1 iPadAir#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 7 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 7 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 7 0 0 0 0 0 0 0
## 9 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 7 0 0 0 0
## 9 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 7 10034 1 iPadAir#0 0 0 0
## 7.1 10034 1 iPadAir#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 7 0 0 0 0 0 0 0
## 7.1 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 7 0 0 0 0 0 0 0
## 7.1 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 7 0 0 0 0 0 0 0
## 7.1 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 7 0 0 0 0
## 7.1 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPadAir#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 30 10114 1 iPadAir#1
## 32 10119 1 iPadAir#1
## descr.my
## 30 comes with: grey ipad divider case, white stylus, Apple USB wall charger and USB cable. HDMI adaptor
## 32 Brand new unopened still sealed ipad air 2.
## D.T.condit D.T.use D.T.new D.T.screen D.T.scratch D.T.good D.T.great
## 30 0 0 0.000000 0 0 0 0
## 32 0 0 1.121774 0 0 0 0
## D.T.excel D.T.work D.T.no D.T.function. D.T.like D.T.crack D.T.fulli
## 30 0 0 0 0 0 0 0
## 32 0 0 0 0 0 0 0
## D.T.box D.T.mint D.T.item D.T.lock D.T.see D.T.icloud D.T.perfect
## 30 0 0 0 0 0 0 0
## 32 0 0 0 0 0 0 0
## D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 30 0 0 0 0 1 0 0
## 32 0 0 1 0 0 0 0
## [1] "min distance(0.8204) pair:"
## UniqueID sold prdl.descr.my.fctr
## 668 11344 1 iPadAir#1
## 974 12097 <NA> iPadAir#1
## descr.my
## 668 Excellent condition and fully functional
## 974 this ipad is fully functional and in excellent condition. icloud has been deactivated and ready for
## D.T.condit D.T.use D.T.new D.T.screen D.T.scratch D.T.good D.T.great
## 668 0.7255333 0 0 0 0 0 0
## 974 0.5804266 0 0 0 0 0 0
## D.T.excel D.T.work D.T.no D.T.function. D.T.like D.T.crack D.T.fulli
## 668 1.1718486 0 0 1.1086571 0 0 1.268015
## 974 0.9374789 0 0 0.8869256 0 0 1.014412
## D.T.box D.T.mint D.T.item D.T.lock D.T.see D.T.icloud D.T.perfect
## 668 0 0 0 0 0 0.000000 0
## 974 0 0 0 0 0 1.184011 0
## D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 668 0 0 0 0 0 0 0
## 974 0 0 0 0 0 0 0
## [1] "Category: iPadAir2#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 20 10090 1 iPadAir2#0 0 0 0
## 22 10092 1 iPadAir2#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 20 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 20 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 20 0 0 0 0 0 0 0
## 22 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 20 0 0 0 0
## 22 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 20 10090 1 iPadAir2#0 0 0 0
## 20.1 10090 1 iPadAir2#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 20 0 0 0 0 0 0 0
## 20.1 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 20 0 0 0 0 0 0 0
## 20.1 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 20 0 0 0 0 0 0 0
## 20.1 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 20 0 0 0 0
## 20.1 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPadAir2#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 77 10198 1 iPadAir2#1
## 175 10342 1 iPadAir2#1
## descr.my
## 77 Clean ESN. iCloud clear. No charger is included. Very good condition. See pictures.
## 175 used. Please read the entire auction. Tablet has only a very light scratches on back of
## D.T.condit D.T.use D.T.new D.T.screen D.T.scratch D.T.good
## 77 0.2902133 0.0000000 0 0 0.0000000 0.4197589
## 175 0.0000000 0.5384593 0 0 0.5879563 0.0000000
## D.T.great D.T.excel D.T.work D.T.no D.T.function. D.T.like
## 77 0 0 0 0.3994056 0 0
## 175 0 0 0 0.0000000 0 0
## D.T.crack D.T.fulli D.T.box D.T.mint D.T.item D.T.lock D.T.see
## 77 0 0 0 0 0 0 0.5335093
## 175 0 0 0 0 0 0 0.0000000
## D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air D.P.black D.P.white
## 77 0.5920055 0 0 0 0 0 0
## 175 0.0000000 0 0 0 0 0 0
## D.P.gold D.P.spacegray
## 77 0 0
## 175 0 0
## [1] "min distance(0.9020) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my
## 604 11115 1 iPadAir2#1 New, sealed, box not included
## 845 11902 <NA> iPadAir2#1 Brand new in sealed retail box.
## D.T.condit D.T.use D.T.new D.T.screen D.T.scratch D.T.good D.T.great
## 604 0 0 1.495699 0 0 0 0
## 845 0 0 1.495699 0 0 0 0
## D.T.excel D.T.work D.T.no D.T.function. D.T.like D.T.crack D.T.fulli
## 604 0 0 0 0 0 0 0
## 845 0 0 0 0 0 0 0
## D.T.box D.T.mint D.T.item D.T.lock D.T.see D.T.icloud D.T.perfect
## 604 1.718173 0 0 0 0 0 0
## 845 1.718173 0 0 0 0 0 0
## D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 604 0 0 0 0 0 0 0
## 845 0 0 0 0 0 0 0
## [1] "Category: iPadmini#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 3 10007 1 iPadmini#0 0 0 0
## 17 10075 1 iPadmini#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 3 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 3 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 3 0 0 0 0 0 0 0
## 17 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 3 0 0 0 0
## 17 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 3 10007 1 iPadmini#0 0 0 0
## 3.1 10007 1 iPadmini#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 3 0 0 0 0 0 0 0
## 3.1 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 3 0 0 0 0 0 0 0
## 3.1 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 3 0 0 0 0 0 0 0
## 3.1 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 3 0 0 0 0
## 3.1 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPadmini#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 29 10113 1 iPadmini#1
## 59 10174 1 iPadmini#1
## descr.my
## 29 READ iPad is used but in excellent condition. Absolutely nothing wrong with it and it has recently
## 59 Please see images and refer closely to the description.
## D.T.condit D.T.use D.T.new D.T.screen D.T.scratch D.T.good D.T.great
## 29 0.4836889 0.5384593 0 0 0 0 0
## 59 0.0000000 0.0000000 0 0 0 0 0
## D.T.excel D.T.work D.T.no D.T.function. D.T.like D.T.crack D.T.fulli
## 29 0.7812324 0 0 0 0 0 0
## 59 0.0000000 0 0 0 0 0 0
## D.T.box D.T.mint D.T.item D.T.lock D.T.see D.T.icloud D.T.perfect
## 29 0 0 0 0 0.000000 0 0
## 59 0 0 0 0 1.333773 0 0
## D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 29 0 0 0 0 0 0 0
## 59 0 0 0 0 0 0 0
## [1] "min distance(0.8090) pair:"
## UniqueID sold prdl.descr.my.fctr
## 802 11723 1 iPadmini#1
## 781 11691 0 iPadmini#1
## descr.my
## 802 Non- functional Unit, handset only. Please refer to the item description below. Contact us with any
## 781 Non- functional Unit, handset only. Please refer to the item description below. Contact us with any
## D.T.condit D.T.use D.T.new D.T.screen D.T.scratch D.T.good D.T.great
## 802 0 0 0 0 0 0 0
## 781 0 0 0 0 0 0 0
## D.T.excel D.T.work D.T.no D.T.function. D.T.like D.T.crack D.T.fulli
## 802 0 0 0 0.4434628 0 0 0
## 781 0 0 0 0.4434628 0 0 0
## D.T.box D.T.mint D.T.item D.T.lock D.T.see D.T.icloud D.T.perfect
## 802 0 0 0.4782552 0 0 0 0
## 781 0 0 0.4782552 0 0 0 0
## D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 802 0 0 0 0 0 0 0
## 781 0 0 0 0 0 0 0
## [1] "Category: iPadmini2#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 2 10006 1 iPadmini2#0 0 0 0
## 10 10056 1 iPadmini2#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 2 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 2 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 2 0 0 0 0 0 0 0
## 10 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 2 0 0 0 0
## 10 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 2 10006 1 iPadmini2#0 0 0 0
## 2.1 10006 1 iPadmini2#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 2 0 0 0 0 0 0 0
## 2.1 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 2 0 0 0 0 0 0 0
## 2.1 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 2 0 0 0 0 0 0 0
## 2.1 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 2 0 0 0 0
## 2.1 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPadmini2#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 335 10573 1 iPadmini2#1
## 375 10636 1 iPadmini2#1
## descr.my
## 335 Like new white iPad mini no scratches always kept in case, sold with keyboard, box and cable
## 375 Used, been in case and screen protector since day 1. Literally flawless!
## D.T.condit D.T.use D.T.new D.T.screen D.T.scratch D.T.good
## 335 0 0.0000000 0.4079178 0.0000000 0.3207034 0
## 375 0 0.5384593 0.0000000 0.6359531 0.0000000 0
## D.T.great D.T.excel D.T.work D.T.no D.T.function. D.T.like
## 335 0 0 0 0.363096 0 0.5089206
## 375 0 0 0 0.000000 0 0.0000000
## D.T.crack D.T.fulli D.T.box D.T.mint D.T.item D.T.lock D.T.see
## 335 0 0 0.4685928 0 0 0 0
## 375 0 0 0.0000000 0 0 0 0
## D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air D.P.black D.P.white
## 335 0 0 0 1 0 0 1
## 375 0 0 0 0 0 0 0
## D.P.gold D.P.spacegray
## 335 0 0
## 375 0 0
## [1] "min distance(0.8608) pair:"
## UniqueID sold prdl.descr.my.fctr
## 753 11652 1 iPadmini2#1
## 930 12031 <NA> iPadmini2#1
## descr.my
## 753 This iPad is functional. However, it has a cracked screen. Includes iPad only. Does not include
## 930 Good Condition(825/ 10), 100% functional with Flawless Retina Display. Unit has a dent on upper left
## D.T.condit D.T.use D.T.new D.T.screen D.T.scratch D.T.good D.T.great
## 753 0.0000000 0 0 0.9539296 0 0.0000000 0
## 930 0.2902133 0 0 0.0000000 0 0.4197589 0
## D.T.excel D.T.work D.T.no D.T.function. D.T.like D.T.crack D.T.fulli
## 753 0 0 0 1.1086571 0 1.258132 0
## 930 0 0 0 0.4434628 0 0.000000 0
## D.T.box D.T.mint D.T.item D.T.lock D.T.see D.T.icloud D.T.perfect
## 753 0 0 0 0 0 0 0
## 930 0 0 0 0 0 0 0
## D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold D.P.spacegray
## 753 0 0 0 0 0 0 0
## 930 0 0 0 0 0 0 0
## [1] "Category: iPadmini3#0"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 62 10177 1 iPadmini3#0 0 0 0
## 104 10245 1 iPadmini3#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 62 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 62 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 62 0 0 0 0 0 0 0
## 104 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 62 0 0 0 0
## 104 0 0 0 0
## [1] "min distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr descr.my D.T.condit D.T.use D.T.new
## 62 10177 1 iPadmini3#0 0 0 0
## 62.1 10177 1 iPadmini3#0 0 0 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 62 0 0 0 0 0 0 0
## 62.1 0 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 62 0 0 0 0 0 0 0
## 62.1 0 0 0 0 0 0 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 62 0 0 0 0 0 0 0
## 62.1 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 62 0 0 0 0
## 62.1 0 0 0 0
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "No module detected"
## [1] "Category: iPadmini3#1"
## [1] "max distance(1.0000) pair:"
## UniqueID sold prdl.descr.my.fctr
## 93 10227 1 iPadmini3#1
## 444 10750 1 iPadmini3#1
## descr.my D.T.condit D.T.use D.T.new
## 93 Used for only two Day mint!! W blue cover 0 0.6461512 0
## 444 Slight crack on very top screen. 0 0.0000000 0
## D.T.screen D.T.scratch D.T.good D.T.great D.T.excel D.T.work D.T.no
## 93 0.0000000 0 0 0 0 0 0
## 444 0.9539296 0 0 0 0 0 0
## D.T.function. D.T.like D.T.crack D.T.fulli D.T.box D.T.mint D.T.item
## 93 0 0 0.000000 0 0 1.198811 0
## 444 0 0 1.258132 0 0 0.000000 0
## D.T.lock D.T.see D.T.icloud D.T.perfect D.P.http D.P.mini D.P.air
## 93 0 0 0 0 0 0 0
## 444 0 0 0 0 0 0 0
## D.P.black D.P.white D.P.gold D.P.spacegray
## 93 0 0 0 0
## 444 0 0 0 0
## [1] "min distance(0.9056) pair:"
## UniqueID sold prdl.descr.my.fctr
## 405 10691 0 iPadmini3#1
## 606 11121 0 iPadmini3#1
## descr.my
## 405 Brand new, never been used. Plastic has not been removed from iPad, however the box is not sealed in
## 606 Bought for one day and removed iPad plastic cover, never used. In original box with all accessories!
## D.T.condit D.T.use D.T.new D.T.screen D.T.scratch D.T.good
## 405 0 0.6461512 0.8974191 0 0 0
## 606 0 0.3589729 0.0000000 0 0 0
## D.T.great D.T.excel D.T.work D.T.no D.T.function. D.T.like D.T.crack
## 405 0 0 0 0 0 0 0
## 606 0 0 0 0 0 0 0
## D.T.fulli D.T.box D.T.mint D.T.item D.T.lock D.T.see D.T.icloud
## 405 0 1.0309041 0 0 0 0 0
## 606 0 0.5727245 0 0 0 0 0
## D.T.perfect D.P.http D.P.mini D.P.air D.P.black D.P.white D.P.gold
## 405 0 0 0 0 0 0 0
## 606 0 0 0 0 0 0 0
## D.P.spacegray
## 405 0
## 606 0
## Source: local data frame [44 x 5]
##
## prdl.descr.my.fctr.clusterid 0 1 .entropy .knt
## (chr) (dbl) (dbl) (dbl) (dbl)
## 1 Unknown#0_1 20 32 0.6662784 52
## 2 Unknown#1_1 2 7 0.5297062 9
## 3 Unknown#1_2 2 4 0.6365142 6
## 4 Unknown#1_3 4 5 0.6869616 9
## 5 Unknown#1_4 0 5 0.0000000 5
## 6 Unknown#1_5 0 3 0.0000000 3
## 7 iPad1#0_1 12 55 0.4700328 67
## 8 iPad1#1_1 6 12 0.6365142 18
## 9 iPad1#1_2 4 16 0.5004024 20
## 10 iPad1#1_3 0 13 0.0000000 13
## .. ... ... ... ... ...
## [1] "glb_allobs_df$prdl.descr.my.fctr$.clusterid Entropy: 0.5022 (94.3896 pct)"# Last call for data modifications
#stop(here") # sav_allobs_df <- glb_allobs_df
# glb_allobs_df[(glb_allobs_df$PropR == 0.75) & (glb_allobs_df$State == "Hawaii"), "PropR.fctr"] <- "N"
# Re-partition
glb_trnobs_df <- subset(glb_allobs_df, .src == "Train")
glb_newobs_df <- subset(glb_allobs_df, .src == "Test")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "partition.data.training", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 7 manage.missing.data 4 1 1 63.861 76.419
## 8 partition.data.training 5 0 0 76.419 NA
## elapsed
## 7 12.558
## 8 NAStep 5.0: partition data training
if (all(is.na(glb_newobs_df[, glb_rsp_var]))) {
set.seed(glb_split_sample.seed)
OOB_size <- nrow(glb_newobs_df) * 1.1
if (is.null(glb_category_var)) {
require(caTools)
split <- sample.split(glb_trnobs_df[, glb_rsp_var_raw],
SplitRatio=OOB_size / nrow(glb_trnobs_df))
glb_OOBobs_df <- glb_trnobs_df[split ,]
glb_fitobs_df <- glb_trnobs_df[!split, ]
} else {
sample_vars <- c(glb_rsp_var_raw, glb_category_var)
rspvar_freq_df <- orderBy(reformulate(glb_rsp_var_raw),
mycreate_sqlxtab_df(glb_trnobs_df, glb_rsp_var_raw))
OOB_rspvar_size <- 1.0 * OOB_size * rspvar_freq_df$.n / sum(rspvar_freq_df$.n)
newobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_newobs_df, glb_category_var))
trnobs_freq_df <- orderBy(reformulate(glb_category_var),
mycreate_sqlxtab_df(glb_trnobs_df, glb_category_var))
allobs_freq_df <- merge(newobs_freq_df, trnobs_freq_df, by=glb_category_var,
all=TRUE, sort=TRUE, suffixes=c(".Tst", ".Train"))
allobs_freq_df[is.na(allobs_freq_df)] <- 0
OOB_strata_size <- ceiling(
as.vector(matrix(allobs_freq_df$.n.Tst * 1.0 / sum(allobs_freq_df$.n.Tst)) %*%
matrix(OOB_rspvar_size, nrow=1)))
OOB_strata_size[OOB_strata_size == 0] <- 1
OOB_strata_df <- expand.grid(glb_rsp_var_raw=rspvar_freq_df[, glb_rsp_var_raw],
glb_category_var=allobs_freq_df[, glb_category_var])
names(OOB_strata_df) <- sample_vars
OOB_strata_df <- orderBy(reformulate(sample_vars), OOB_strata_df)
trnobs_univ_df <- orderBy(reformulate(sample_vars),
mycreate_sqlxtab_df(glb_trnobs_df, sample_vars))
trnobs_univ_df <- merge(trnobs_univ_df, OOB_strata_df, all=TRUE)
tmp_trnobs_df <- orderBy(reformulate(c(glb_rsp_var_raw, glb_category_var)),
glb_trnobs_df)
require(sampling)
split_strata <- strata(tmp_trnobs_df,
stratanames=c(glb_rsp_var_raw, glb_category_var),
size=OOB_strata_size[!is.na(trnobs_univ_df$.n)],
method="srswor")
glb_OOBobs_df <- getdata(tmp_trnobs_df, split_strata)[, names(glb_trnobs_df)]
glb_fitobs_df <- glb_trnobs_df[!glb_trnobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ]
}
} else {
print(sprintf("Newdata contains non-NA data for %s; setting OOB to Newdata",
glb_rsp_var))
glb_fitobs_df <- glb_trnobs_df; glb_OOBobs_df <- glb_newobs_df
}## [1] "Newdata contains non-NA data for startprice.log10; setting OOB to Newdata"if (!is.null(glb_max_fitobs) && (nrow(glb_fitobs_df) > glb_max_fitobs)) {
warning("glb_fitobs_df restricted to glb_max_fitobs: ",
format(glb_max_fitobs, big.mark=","))
org_fitobs_df <- glb_fitobs_df
glb_fitobs_df <-
org_fitobs_df[split <- sample.split(org_fitobs_df[, glb_rsp_var_raw],
SplitRatio=glb_max_fitobs), ]
org_fitobs_df <- NULL
}
if (!is.null(glb_obsfit_outliers)) {
glb_OOBobs_df <- rbind(glb_OOBobs_df,
glb_fitobs_df[glb_fitobs_df[, glb_id_var] %in% glb_obsfit_outliers, ])
glb_fitobs_df <- glb_fitobs_df[!(glb_fitobs_df[, glb_id_var] %in% glb_obsfit_outliers), ]
}
glb_allobs_df$.lcn <- ""; glb_trnobs_df$.lcn <- "";
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_trnobs_df[glb_trnobs_df[, glb_id_var] %in%
glb_fitobs_df[, glb_id_var], ".lcn"] <- "Fit"
glb_allobs_df[glb_allobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
glb_trnobs_df[glb_trnobs_df[, glb_id_var] %in%
glb_OOBobs_df[, glb_id_var], ".lcn"] <- "OOB"
dsp_class_dstrb <- function(obs_df, location_var, partition_var) {
xtab_df <- mycreate_xtab_df(obs_df, c(location_var, partition_var))
rownames(xtab_df) <- xtab_df[, location_var]
xtab_df <- xtab_df[, -grepl(location_var, names(xtab_df))]
print(xtab_df)
print(xtab_df / rowSums(xtab_df, na.rm=TRUE))
}
# Ensure proper splits by glb_rsp_var_raw & user-specified feature for OOB vs. new
if (!is.null(glb_category_var)) {
if (glb_is_classification)
dsp_class_dstrb(glb_allobs_df, ".lcn", glb_rsp_var_raw)
newobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .src == "Test"),
glb_category_var)
OOBobs_ctgry_df <- mycreate_sqlxtab_df(subset(glb_allobs_df, .lcn == "OOB"),
glb_category_var)
glb_ctgry_df <- merge(newobs_ctgry_df, OOBobs_ctgry_df, by=glb_category_var
, all=TRUE, suffixes=c(".Tst", ".OOB"))
glb_ctgry_df$.freqRatio.Tst <- glb_ctgry_df$.n.Tst / sum(glb_ctgry_df$.n.Tst, na.rm=TRUE)
glb_ctgry_df$.freqRatio.OOB <- glb_ctgry_df$.n.OOB / sum(glb_ctgry_df$.n.OOB, na.rm=TRUE)
print(orderBy(~-.freqRatio.Tst-.freqRatio.OOB, glb_ctgry_df))
}## prdl.descr.my.fctr .n.Tst .n.OOB .freqRatio.Tst .freqRatio.OOB
## 5 iPad2#0 68 71 0.118673647 0.120338983
## 15 iPadmini#0 62 63 0.108202443 0.106779661
## 3 iPad1#0 42 43 0.073298429 0.072881356
## 16 iPadmini#1 34 35 0.059336824 0.059322034
## 1 Unknown#0 34 34 0.059336824 0.057627119
## 2 Unknown#1 32 32 0.055846422 0.054237288
## 13 iPadAir2#0 32 32 0.055846422 0.054237288
## 4 iPad1#1 30 31 0.052356021 0.052542373
## 11 iPadAir#0 29 29 0.050610820 0.049152542
## 17 iPadmini2#0 28 29 0.048865620 0.049152542
## 6 iPad2#1 27 32 0.047120419 0.054237288
## 7 iPad3#0 27 27 0.047120419 0.045762712
## 10 iPad4#1 25 25 0.043630017 0.042372881
## 19 iPadmini3#0 21 22 0.036649215 0.037288136
## 9 iPad4#0 20 21 0.034904014 0.035593220
## 12 iPadAir#1 20 20 0.034904014 0.033898305
## 18 iPadmini2#1 18 19 0.031413613 0.032203390
## 8 iPad3#1 10 11 0.017452007 0.018644068
## 14 iPadAir2#1 9 9 0.015706806 0.015254237
## 20 iPadmini3#1 5 5 0.008726003 0.008474576print("glb_allobs_df: "); print(dim(glb_allobs_df))## [1] "glb_allobs_df: "## [1] 1211 106print("glb_trnobs_df: "); print(dim(glb_trnobs_df))## [1] "glb_trnobs_df: "## [1] 638 106print("glb_fitobs_df: "); print(dim(glb_fitobs_df))## [1] "glb_fitobs_df: "## [1] 621 105print("glb_OOBobs_df: "); print(dim(glb_OOBobs_df))## [1] "glb_OOBobs_df: "## [1] 590 105print("glb_newobs_df: "); print(dim(glb_newobs_df))## [1] "glb_newobs_df: "## [1] 573 105# # Does not handle NULL or length(glb_id_var) > 1
if (glb_save_envir)
save(glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "blddfs_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
rm(split)## Warning in rm(split): object 'split' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "select.features", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 8 partition.data.training 5 0 0 76.419 81.578
## 9 select.features 6 0 0 81.578 NA
## elapsed
## 8 5.159
## 9 NAStep 6.0: select features
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
print(glb_feats_df <- myselect_features(entity_df=glb_trnobs_df,
exclude_vars_as_features=glb_exclude_vars_as_features,
rsp_var=glb_rsp_var))## Warning in cor(data.matrix(entity_df[, sel_feats]), y =
## as.numeric(entity_df[, : the standard deviation is zero## id cor.y
## startprice.log10.cut.fctr startprice.log10.cut.fctr 9.475377e-01
## startprice startprice 7.113445e-01
## startprice.dcm2.is9 startprice.dcm2.is9 -4.608606e-01
## startprice.dcm1.is9 startprice.dcm1.is9 -4.333145e-01
## startprice.dgt1.is9 startprice.dgt1.is9 2.793023e-01
## startprice.dgt2.is9 startprice.dgt2.is9 2.458923e-01
## D.T.function. D.T.function. -2.207568e-01
## UniqueID UniqueID -1.959774e-01
## .clusterid .clusterid -1.821334e-01
## .clusterid.fctr .clusterid.fctr -1.821334e-01
## D.T.fulli D.T.fulli -1.802946e-01
## D.ratio.weight.sum.wrds.n D.ratio.weight.sum.wrds.n -1.697384e-01
## D.ratio.wrds.stop.n.wrds.n D.ratio.wrds.stop.n.wrds.n 1.621894e-01
## D.chrs.uppr.n.log D.chrs.uppr.n.log -1.493025e-01
## D.terms.post.stem.n.log D.terms.post.stem.n.log -1.488388e-01
## D.wrds.unq.n.log D.wrds.unq.n.log -1.488388e-01
## D.weight.post.stem.sum D.weight.post.stem.sum -1.486081e-01
## D.weight.sum D.weight.sum -1.486081e-01
## D.terms.post.stop.n.log D.terms.post.stop.n.log -1.485640e-01
## D.weight.post.stop.sum D.weight.post.stop.sum -1.484270e-01
## D.chrs.n.log D.chrs.n.log -1.478757e-01
## D.terms.post.stem.n D.terms.post.stem.n -1.400178e-01
## D.terms.post.stop.n D.terms.post.stop.n -1.394723e-01
## D.wrds.n.log D.wrds.n.log -1.307835e-01
## prdl.descr.my.fctr prdl.descr.my.fctr 1.174257e-01
## D.T.box D.T.box -1.086714e-01
## D.T.screen D.T.screen -1.068446e-01
## D.chrs.pnct13.n.log D.chrs.pnct13.n.log -1.024708e-01
## D.wrds.stop.n.log D.wrds.stop.n.log -9.727686e-02
## D.T.good D.T.good -9.655397e-02
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio 7.870678e-02
## D.chrs.pnct11.n.log D.chrs.pnct11.n.log -7.800598e-02
## D.T.no D.T.no -7.226042e-02
## D.chrs.pnct05.n.log D.chrs.pnct05.n.log -7.068660e-02
## D.T.condit D.T.condit -6.648443e-02
## cellular.fctr cellular.fctr -6.593839e-02
## D.T.mint D.T.mint 6.497510e-02
## D.chrs.pnct15.n.log D.chrs.pnct15.n.log 5.993738e-02
## D.chrs.pnct12.n.log D.chrs.pnct12.n.log -5.639687e-02
## D.T.crack D.T.crack -5.082401e-02
## D.chrs.pnct07.n.log D.chrs.pnct07.n.log -4.994372e-02
## D.chrs.pnct14.n.log D.chrs.pnct14.n.log -4.836149e-02
## D.T.use D.T.use 4.704955e-02
## condition.fctr condition.fctr -4.529371e-02
## D.P.mini D.P.mini -4.179600e-02
## D.T.great D.T.great -4.080366e-02
## carrier.fctr carrier.fctr -4.045293e-02
## D.T.like D.T.like 3.708720e-02
## D.T.item D.T.item -3.660548e-02
## D.T.see D.T.see -3.616379e-02
## D.chrs.pnct01.n.log D.chrs.pnct01.n.log 3.449116e-02
## D.T.excel D.T.excel -2.909137e-02
## storage.fctr storage.fctr 2.907359e-02
## .rnorm .rnorm 2.906750e-02
## D.P.white D.P.white -2.823774e-02
## D.dgts.n.log D.dgts.n.log -2.815344e-02
## D.P.black D.P.black -2.110770e-02
## D.T.new D.T.new 1.938403e-02
## D.chrs.pnct03.n.log D.chrs.pnct03.n.log -1.902309e-02
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio 1.480705e-02
## D.T.lock D.T.lock 1.259657e-02
## D.P.air D.P.air 1.121413e-02
## D.T.work D.T.work 1.082156e-02
## D.chrs.pnct06.n.log D.chrs.pnct06.n.log 1.029894e-02
## D.P.spacegray D.P.spacegray 8.461024e-03
## color.fctr color.fctr -7.940197e-03
## D.T.icloud D.T.icloud -7.105792e-03
## D.T.scratch D.T.scratch 6.098563e-03
## D.chrs.pnct16.n.log D.chrs.pnct16.n.log -3.734420e-03
## D.T.perfect D.T.perfect -2.582106e-03
## D.chrs.pnct08.n.log D.chrs.pnct08.n.log 7.742276e-05
## biddable biddable NA
## sold sold NA
## D.chrs.pnct02.n.log D.chrs.pnct02.n.log NA
## D.chrs.pnct04.n.log D.chrs.pnct04.n.log NA
## D.chrs.pnct09.n.log D.chrs.pnct09.n.log NA
## D.chrs.pnct10.n.log D.chrs.pnct10.n.log NA
## D.chrs.pnct17.n.log D.chrs.pnct17.n.log NA
## D.chrs.pnct18.n.log D.chrs.pnct18.n.log NA
## D.chrs.pnct19.n.log D.chrs.pnct19.n.log NA
## D.chrs.pnct20.n.log D.chrs.pnct20.n.log NA
## D.chrs.pnct21.n.log D.chrs.pnct21.n.log NA
## D.chrs.pnct22.n.log D.chrs.pnct22.n.log NA
## D.chrs.pnct23.n.log D.chrs.pnct23.n.log NA
## D.chrs.pnct24.n.log D.chrs.pnct24.n.log NA
## D.chrs.pnct25.n.log D.chrs.pnct25.n.log NA
## D.chrs.pnct26.n.log D.chrs.pnct26.n.log NA
## D.chrs.pnct27.n.log D.chrs.pnct27.n.log NA
## D.chrs.pnct28.n.log D.chrs.pnct28.n.log NA
## D.chrs.pnct29.n.log D.chrs.pnct29.n.log NA
## D.chrs.pnct30.n.log D.chrs.pnct30.n.log NA
## D.P.http D.P.http NA
## D.P.gold D.P.gold NA
## startprice.dgt3.is9 startprice.dgt3.is9 NA
## exclude.as.feat cor.y.abs
## startprice.log10.cut.fctr 1 9.475377e-01
## startprice 1 7.113445e-01
## startprice.dcm2.is9 0 4.608606e-01
## startprice.dcm1.is9 0 4.333145e-01
## startprice.dgt1.is9 0 2.793023e-01
## startprice.dgt2.is9 0 2.458923e-01
## D.T.function. 1 2.207568e-01
## UniqueID 1 1.959774e-01
## .clusterid 1 1.821334e-01
## .clusterid.fctr 0 1.821334e-01
## D.T.fulli 1 1.802946e-01
## D.ratio.weight.sum.wrds.n 0 1.697384e-01
## D.ratio.wrds.stop.n.wrds.n 0 1.621894e-01
## D.chrs.uppr.n.log 0 1.493025e-01
## D.terms.post.stem.n.log 0 1.488388e-01
## D.wrds.unq.n.log 0 1.488388e-01
## D.weight.post.stem.sum 0 1.486081e-01
## D.weight.sum 0 1.486081e-01
## D.terms.post.stop.n.log 0 1.485640e-01
## D.weight.post.stop.sum 0 1.484270e-01
## D.chrs.n.log 0 1.478757e-01
## D.terms.post.stem.n 1 1.400178e-01
## D.terms.post.stop.n 1 1.394723e-01
## D.wrds.n.log 0 1.307835e-01
## prdl.descr.my.fctr 0 1.174257e-01
## D.T.box 1 1.086714e-01
## D.T.screen 1 1.068446e-01
## D.chrs.pnct13.n.log 0 1.024708e-01
## D.wrds.stop.n.log 0 9.727686e-02
## D.T.good 1 9.655397e-02
## D.weight.sum.stem.stop.Ratio 0 7.870678e-02
## D.chrs.pnct11.n.log 0 7.800598e-02
## D.T.no 1 7.226042e-02
## D.chrs.pnct05.n.log 0 7.068660e-02
## D.T.condit 1 6.648443e-02
## cellular.fctr 0 6.593839e-02
## D.T.mint 1 6.497510e-02
## D.chrs.pnct15.n.log 0 5.993738e-02
## D.chrs.pnct12.n.log 0 5.639687e-02
## D.T.crack 1 5.082401e-02
## D.chrs.pnct07.n.log 0 4.994372e-02
## D.chrs.pnct14.n.log 0 4.836149e-02
## D.T.use 1 4.704955e-02
## condition.fctr 0 4.529371e-02
## D.P.mini 1 4.179600e-02
## D.T.great 1 4.080366e-02
## carrier.fctr 0 4.045293e-02
## D.T.like 1 3.708720e-02
## D.T.item 1 3.660548e-02
## D.T.see 1 3.616379e-02
## D.chrs.pnct01.n.log 0 3.449116e-02
## D.T.excel 1 2.909137e-02
## storage.fctr 0 2.907359e-02
## .rnorm 0 2.906750e-02
## D.P.white 1 2.823774e-02
## D.dgts.n.log 0 2.815344e-02
## D.P.black 1 2.110770e-02
## D.T.new 1 1.938403e-02
## D.chrs.pnct03.n.log 0 1.902309e-02
## D.terms.n.stem.stop.Ratio 0 1.480705e-02
## D.T.lock 1 1.259657e-02
## D.P.air 1 1.121413e-02
## D.T.work 1 1.082156e-02
## D.chrs.pnct06.n.log 0 1.029894e-02
## D.P.spacegray 1 8.461024e-03
## color.fctr 0 7.940197e-03
## D.T.icloud 1 7.105792e-03
## D.T.scratch 1 6.098563e-03
## D.chrs.pnct16.n.log 0 3.734420e-03
## D.T.perfect 1 2.582106e-03
## D.chrs.pnct08.n.log 0 7.742276e-05
## biddable 0 NA
## sold 1 NA
## D.chrs.pnct02.n.log 0 NA
## D.chrs.pnct04.n.log 0 NA
## D.chrs.pnct09.n.log 0 NA
## D.chrs.pnct10.n.log 0 NA
## D.chrs.pnct17.n.log 0 NA
## D.chrs.pnct18.n.log 0 NA
## D.chrs.pnct19.n.log 0 NA
## D.chrs.pnct20.n.log 0 NA
## D.chrs.pnct21.n.log 0 NA
## D.chrs.pnct22.n.log 0 NA
## D.chrs.pnct23.n.log 0 NA
## D.chrs.pnct24.n.log 0 NA
## D.chrs.pnct25.n.log 0 NA
## D.chrs.pnct26.n.log 0 NA
## D.chrs.pnct27.n.log 0 NA
## D.chrs.pnct28.n.log 0 NA
## D.chrs.pnct29.n.log 0 NA
## D.chrs.pnct30.n.log 0 NA
## D.P.http 1 NA
## D.P.gold 1 NA
## startprice.dgt3.is9 0 NAprint(glb_feats_df <- orderBy(~-cor.y,
myfind_cor_features(feats_df=glb_feats_df, obs_df=glb_trnobs_df, rsp_var=glb_rsp_var,
nzv.freqCut=glb_nzv_freqCut, nzv.uniqueCut=glb_nzv_uniqueCut)))## [1] "cor(D.terms.post.stem.n.log, D.wrds.unq.n.log)=1.0000"
## [1] "cor(startprice.log10, D.terms.post.stem.n.log)=-0.1488"
## [1] "cor(startprice.log10, D.wrds.unq.n.log)=-0.1488"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.wrds.unq.n.log as highly correlated with
## D.terms.post.stem.n.log## [1] "cor(D.weight.post.stem.sum, D.weight.sum)=1.0000"
## [1] "cor(startprice.log10, D.weight.post.stem.sum)=-0.1486"
## [1] "cor(startprice.log10, D.weight.sum)=-0.1486"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.weight.sum as highly correlated with
## D.weight.post.stem.sum## [1] "cor(D.terms.post.stem.n.log, D.terms.post.stop.n.log)=0.9999"
## [1] "cor(startprice.log10, D.terms.post.stem.n.log)=-0.1488"
## [1] "cor(startprice.log10, D.terms.post.stop.n.log)=-0.1486"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.post.stop.n.log as highly correlated
## with D.terms.post.stem.n.log## [1] "cor(D.chrs.n.log, D.chrs.uppr.n.log)=0.9998"
## [1] "cor(startprice.log10, D.chrs.n.log)=-0.1479"
## [1] "cor(startprice.log10, D.chrs.uppr.n.log)=-0.1493"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.chrs.n.log as highly correlated with
## D.chrs.uppr.n.log## [1] "cor(D.weight.post.stem.sum, D.weight.post.stop.sum)=0.9988"
## [1] "cor(startprice.log10, D.weight.post.stem.sum)=-0.1486"
## [1] "cor(startprice.log10, D.weight.post.stop.sum)=-0.1484"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.weight.post.stop.sum as highly correlated
## with D.weight.post.stem.sum## [1] "cor(D.chrs.uppr.n.log, D.wrds.n.log)=0.9901"
## [1] "cor(startprice.log10, D.chrs.uppr.n.log)=-0.1493"
## [1] "cor(startprice.log10, D.wrds.n.log)=-0.1308"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.wrds.n.log as highly correlated with
## D.chrs.uppr.n.log## [1] "cor(D.chrs.uppr.n.log, D.terms.post.stem.n.log)=0.9788"
## [1] "cor(startprice.log10, D.chrs.uppr.n.log)=-0.1493"
## [1] "cor(startprice.log10, D.terms.post.stem.n.log)=-0.1488"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.terms.post.stem.n.log as highly correlated
## with D.chrs.uppr.n.log## [1] "cor(D.chrs.uppr.n.log, D.weight.post.stem.sum)=0.9681"
## [1] "cor(startprice.log10, D.chrs.uppr.n.log)=-0.1493"
## [1] "cor(startprice.log10, D.weight.post.stem.sum)=-0.1486"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.weight.post.stem.sum as highly correlated
## with D.chrs.uppr.n.log## [1] "cor(D.chrs.uppr.n.log, D.ratio.wrds.stop.n.wrds.n)=-0.9204"
## [1] "cor(startprice.log10, D.chrs.uppr.n.log)=-0.1493"
## [1] "cor(startprice.log10, D.ratio.wrds.stop.n.wrds.n)=0.1622"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.chrs.uppr.n.log as highly correlated with
## D.ratio.wrds.stop.n.wrds.n## [1] "cor(startprice.dcm1.is9, startprice.dcm2.is9)=0.9152"
## [1] "cor(startprice.log10, startprice.dcm1.is9)=-0.4333"
## [1] "cor(startprice.log10, startprice.dcm2.is9)=-0.4609"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified startprice.dcm1.is9 as highly correlated with
## startprice.dcm2.is9## [1] "cor(D.chrs.pnct13.n.log, D.wrds.stop.n.log)=0.7128"
## [1] "cor(startprice.log10, D.chrs.pnct13.n.log)=-0.1025"
## [1] "cor(startprice.log10, D.wrds.stop.n.log)=-0.0973"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified D.wrds.stop.n.log as highly correlated with
## D.chrs.pnct13.n.log## [1] "cor(carrier.fctr, cellular.fctr)=0.7120"
## [1] "cor(startprice.log10, carrier.fctr)=-0.0405"
## [1] "cor(startprice.log10, cellular.fctr)=-0.0659"## Warning in myfind_cor_features(feats_df = glb_feats_df, obs_df =
## glb_trnobs_df, : Identified carrier.fctr as highly correlated with
## cellular.fctr## id cor.y
## startprice.log10.cut.fctr startprice.log10.cut.fctr 9.475377e-01
## startprice startprice 7.113445e-01
## startprice.dgt1.is9 startprice.dgt1.is9 2.793023e-01
## startprice.dgt2.is9 startprice.dgt2.is9 2.458923e-01
## D.ratio.wrds.stop.n.wrds.n D.ratio.wrds.stop.n.wrds.n 1.621894e-01
## prdl.descr.my.fctr prdl.descr.my.fctr 1.174257e-01
## D.weight.sum.stem.stop.Ratio D.weight.sum.stem.stop.Ratio 7.870678e-02
## D.T.mint D.T.mint 6.497510e-02
## D.chrs.pnct15.n.log D.chrs.pnct15.n.log 5.993738e-02
## D.T.use D.T.use 4.704955e-02
## D.T.like D.T.like 3.708720e-02
## D.chrs.pnct01.n.log D.chrs.pnct01.n.log 3.449116e-02
## storage.fctr storage.fctr 2.907359e-02
## .rnorm .rnorm 2.906750e-02
## D.T.new D.T.new 1.938403e-02
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio 1.480705e-02
## D.T.lock D.T.lock 1.259657e-02
## D.P.air D.P.air 1.121413e-02
## D.T.work D.T.work 1.082156e-02
## D.chrs.pnct06.n.log D.chrs.pnct06.n.log 1.029894e-02
## D.P.spacegray D.P.spacegray 8.461024e-03
## D.T.scratch D.T.scratch 6.098563e-03
## D.chrs.pnct08.n.log D.chrs.pnct08.n.log 7.742276e-05
## D.T.perfect D.T.perfect -2.582106e-03
## D.chrs.pnct16.n.log D.chrs.pnct16.n.log -3.734420e-03
## D.T.icloud D.T.icloud -7.105792e-03
## color.fctr color.fctr -7.940197e-03
## D.chrs.pnct03.n.log D.chrs.pnct03.n.log -1.902309e-02
## D.P.black D.P.black -2.110770e-02
## D.dgts.n.log D.dgts.n.log -2.815344e-02
## D.P.white D.P.white -2.823774e-02
## D.T.excel D.T.excel -2.909137e-02
## D.T.see D.T.see -3.616379e-02
## D.T.item D.T.item -3.660548e-02
## carrier.fctr carrier.fctr -4.045293e-02
## D.T.great D.T.great -4.080366e-02
## D.P.mini D.P.mini -4.179600e-02
## condition.fctr condition.fctr -4.529371e-02
## D.chrs.pnct14.n.log D.chrs.pnct14.n.log -4.836149e-02
## D.chrs.pnct07.n.log D.chrs.pnct07.n.log -4.994372e-02
## D.T.crack D.T.crack -5.082401e-02
## D.chrs.pnct12.n.log D.chrs.pnct12.n.log -5.639687e-02
## cellular.fctr cellular.fctr -6.593839e-02
## D.T.condit D.T.condit -6.648443e-02
## D.chrs.pnct05.n.log D.chrs.pnct05.n.log -7.068660e-02
## D.T.no D.T.no -7.226042e-02
## D.chrs.pnct11.n.log D.chrs.pnct11.n.log -7.800598e-02
## D.T.good D.T.good -9.655397e-02
## D.wrds.stop.n.log D.wrds.stop.n.log -9.727686e-02
## D.chrs.pnct13.n.log D.chrs.pnct13.n.log -1.024708e-01
## D.T.screen D.T.screen -1.068446e-01
## D.T.box D.T.box -1.086714e-01
## D.wrds.n.log D.wrds.n.log -1.307835e-01
## D.terms.post.stop.n D.terms.post.stop.n -1.394723e-01
## D.terms.post.stem.n D.terms.post.stem.n -1.400178e-01
## D.chrs.n.log D.chrs.n.log -1.478757e-01
## D.weight.post.stop.sum D.weight.post.stop.sum -1.484270e-01
## D.terms.post.stop.n.log D.terms.post.stop.n.log -1.485640e-01
## D.weight.post.stem.sum D.weight.post.stem.sum -1.486081e-01
## D.weight.sum D.weight.sum -1.486081e-01
## D.terms.post.stem.n.log D.terms.post.stem.n.log -1.488388e-01
## D.wrds.unq.n.log D.wrds.unq.n.log -1.488388e-01
## D.chrs.uppr.n.log D.chrs.uppr.n.log -1.493025e-01
## D.ratio.weight.sum.wrds.n D.ratio.weight.sum.wrds.n -1.697384e-01
## D.T.fulli D.T.fulli -1.802946e-01
## .clusterid .clusterid -1.821334e-01
## .clusterid.fctr .clusterid.fctr -1.821334e-01
## UniqueID UniqueID -1.959774e-01
## D.T.function. D.T.function. -2.207568e-01
## startprice.dcm1.is9 startprice.dcm1.is9 -4.333145e-01
## startprice.dcm2.is9 startprice.dcm2.is9 -4.608606e-01
## D.P.gold D.P.gold NA
## D.P.http D.P.http NA
## D.chrs.pnct02.n.log D.chrs.pnct02.n.log NA
## D.chrs.pnct04.n.log D.chrs.pnct04.n.log NA
## D.chrs.pnct09.n.log D.chrs.pnct09.n.log NA
## D.chrs.pnct10.n.log D.chrs.pnct10.n.log NA
## D.chrs.pnct17.n.log D.chrs.pnct17.n.log NA
## D.chrs.pnct18.n.log D.chrs.pnct18.n.log NA
## D.chrs.pnct19.n.log D.chrs.pnct19.n.log NA
## D.chrs.pnct20.n.log D.chrs.pnct20.n.log NA
## D.chrs.pnct21.n.log D.chrs.pnct21.n.log NA
## D.chrs.pnct22.n.log D.chrs.pnct22.n.log NA
## D.chrs.pnct23.n.log D.chrs.pnct23.n.log NA
## D.chrs.pnct24.n.log D.chrs.pnct24.n.log NA
## D.chrs.pnct25.n.log D.chrs.pnct25.n.log NA
## D.chrs.pnct26.n.log D.chrs.pnct26.n.log NA
## D.chrs.pnct27.n.log D.chrs.pnct27.n.log NA
## D.chrs.pnct28.n.log D.chrs.pnct28.n.log NA
## D.chrs.pnct29.n.log D.chrs.pnct29.n.log NA
## D.chrs.pnct30.n.log D.chrs.pnct30.n.log NA
## biddable biddable NA
## sold sold NA
## startprice.dgt3.is9 startprice.dgt3.is9 NA
## exclude.as.feat cor.y.abs
## startprice.log10.cut.fctr 1 9.475377e-01
## startprice 1 7.113445e-01
## startprice.dgt1.is9 0 2.793023e-01
## startprice.dgt2.is9 0 2.458923e-01
## D.ratio.wrds.stop.n.wrds.n 0 1.621894e-01
## prdl.descr.my.fctr 0 1.174257e-01
## D.weight.sum.stem.stop.Ratio 0 7.870678e-02
## D.T.mint 1 6.497510e-02
## D.chrs.pnct15.n.log 0 5.993738e-02
## D.T.use 1 4.704955e-02
## D.T.like 1 3.708720e-02
## D.chrs.pnct01.n.log 0 3.449116e-02
## storage.fctr 0 2.907359e-02
## .rnorm 0 2.906750e-02
## D.T.new 1 1.938403e-02
## D.terms.n.stem.stop.Ratio 0 1.480705e-02
## D.T.lock 1 1.259657e-02
## D.P.air 1 1.121413e-02
## D.T.work 1 1.082156e-02
## D.chrs.pnct06.n.log 0 1.029894e-02
## D.P.spacegray 1 8.461024e-03
## D.T.scratch 1 6.098563e-03
## D.chrs.pnct08.n.log 0 7.742276e-05
## D.T.perfect 1 2.582106e-03
## D.chrs.pnct16.n.log 0 3.734420e-03
## D.T.icloud 1 7.105792e-03
## color.fctr 0 7.940197e-03
## D.chrs.pnct03.n.log 0 1.902309e-02
## D.P.black 1 2.110770e-02
## D.dgts.n.log 0 2.815344e-02
## D.P.white 1 2.823774e-02
## D.T.excel 1 2.909137e-02
## D.T.see 1 3.616379e-02
## D.T.item 1 3.660548e-02
## carrier.fctr 0 4.045293e-02
## D.T.great 1 4.080366e-02
## D.P.mini 1 4.179600e-02
## condition.fctr 0 4.529371e-02
## D.chrs.pnct14.n.log 0 4.836149e-02
## D.chrs.pnct07.n.log 0 4.994372e-02
## D.T.crack 1 5.082401e-02
## D.chrs.pnct12.n.log 0 5.639687e-02
## cellular.fctr 0 6.593839e-02
## D.T.condit 1 6.648443e-02
## D.chrs.pnct05.n.log 0 7.068660e-02
## D.T.no 1 7.226042e-02
## D.chrs.pnct11.n.log 0 7.800598e-02
## D.T.good 1 9.655397e-02
## D.wrds.stop.n.log 0 9.727686e-02
## D.chrs.pnct13.n.log 0 1.024708e-01
## D.T.screen 1 1.068446e-01
## D.T.box 1 1.086714e-01
## D.wrds.n.log 0 1.307835e-01
## D.terms.post.stop.n 1 1.394723e-01
## D.terms.post.stem.n 1 1.400178e-01
## D.chrs.n.log 0 1.478757e-01
## D.weight.post.stop.sum 0 1.484270e-01
## D.terms.post.stop.n.log 0 1.485640e-01
## D.weight.post.stem.sum 0 1.486081e-01
## D.weight.sum 0 1.486081e-01
## D.terms.post.stem.n.log 0 1.488388e-01
## D.wrds.unq.n.log 0 1.488388e-01
## D.chrs.uppr.n.log 0 1.493025e-01
## D.ratio.weight.sum.wrds.n 0 1.697384e-01
## D.T.fulli 1 1.802946e-01
## .clusterid 1 1.821334e-01
## .clusterid.fctr 0 1.821334e-01
## UniqueID 1 1.959774e-01
## D.T.function. 1 2.207568e-01
## startprice.dcm1.is9 0 4.333145e-01
## startprice.dcm2.is9 0 4.608606e-01
## D.P.gold 1 NA
## D.P.http 1 NA
## D.chrs.pnct02.n.log 0 NA
## D.chrs.pnct04.n.log 0 NA
## D.chrs.pnct09.n.log 0 NA
## D.chrs.pnct10.n.log 0 NA
## D.chrs.pnct17.n.log 0 NA
## D.chrs.pnct18.n.log 0 NA
## D.chrs.pnct19.n.log 0 NA
## D.chrs.pnct20.n.log 0 NA
## D.chrs.pnct21.n.log 0 NA
## D.chrs.pnct22.n.log 0 NA
## D.chrs.pnct23.n.log 0 NA
## D.chrs.pnct24.n.log 0 NA
## D.chrs.pnct25.n.log 0 NA
## D.chrs.pnct26.n.log 0 NA
## D.chrs.pnct27.n.log 0 NA
## D.chrs.pnct28.n.log 0 NA
## D.chrs.pnct29.n.log 0 NA
## D.chrs.pnct30.n.log 0 NA
## biddable 0 NA
## sold 1 NA
## startprice.dgt3.is9 0 NA
## cor.high.X freqRatio
## startprice.log10.cut.fctr <NA> 2.212435
## startprice <NA> 4.000000
## startprice.dgt1.is9 <NA> 3.589928
## startprice.dgt2.is9 <NA> 8.968750
## D.ratio.wrds.stop.n.wrds.n <NA> 13.000000
## prdl.descr.my.fctr <NA> 1.135593
## D.weight.sum.stem.stop.Ratio <NA> 91.200000
## D.T.mint <NA> 315.000000
## D.chrs.pnct15.n.log <NA> 158.500000
## D.T.use <NA> 52.090909
## D.T.like <NA> 314.500000
## D.chrs.pnct01.n.log <NA> 40.933333
## storage.fctr <NA> 2.837398
## .rnorm <NA> 1.000000
## D.T.new <NA> 102.000000
## D.terms.n.stem.stop.Ratio <NA> 104.333333
## D.T.lock <NA> 208.666667
## D.P.air <NA> 105.333333
## D.T.work <NA> 85.142857
## D.chrs.pnct06.n.log <NA> 51.500000
## D.P.spacegray <NA> 318.000000
## D.T.scratch <NA> 57.900000
## D.chrs.pnct08.n.log <NA> 69.666667
## D.T.perfect <NA> 157.000000
## D.chrs.pnct16.n.log <NA> 51.416667
## D.T.icloud <NA> 313.500000
## color.fctr <NA> 1.490798
## D.chrs.pnct03.n.log <NA> 69.333333
## D.P.black <NA> 158.500000
## D.dgts.n.log <NA> 50.083333
## D.P.white <NA> 211.333333
## D.T.excel <NA> 154.000000
## D.T.see <NA> 208.000000
## D.T.item <NA> 87.571429
## carrier.fctr cellular.fctr 4.265306
## D.T.great <NA> 122.400000
## D.P.mini <NA> 105.333333
## condition.fctr <NA> 5.433735
## D.chrs.pnct14.n.log <NA> 78.750000
## D.chrs.pnct07.n.log <NA> 637.000000
## D.T.crack <NA> 123.400000
## D.chrs.pnct12.n.log <NA> 29.238095
## cellular.fctr <NA> 2.596273
## D.T.condit <NA> 37.266667
## D.chrs.pnct05.n.log <NA> 318.000000
## D.T.no <NA> 45.769231
## D.chrs.pnct11.n.log <NA> 8.730159
## D.T.good <NA> 85.714286
## D.wrds.stop.n.log D.chrs.pnct13.n.log 18.521739
## D.chrs.pnct13.n.log <NA> 6.246753
## D.T.screen <NA> 52.727273
## D.T.box <NA> 124.000000
## D.wrds.n.log D.chrs.uppr.n.log 13.448276
## D.terms.post.stop.n <NA> 10.025641
## D.terms.post.stem.n <NA> 9.775000
## D.chrs.n.log D.chrs.uppr.n.log 26.000000
## D.weight.post.stop.sum D.weight.post.stem.sum 78.200000
## D.terms.post.stop.n.log D.terms.post.stem.n.log 10.025641
## D.weight.post.stem.sum D.chrs.uppr.n.log 78.200000
## D.weight.sum D.weight.post.stem.sum 78.200000
## D.terms.post.stem.n.log D.chrs.uppr.n.log 9.775000
## D.wrds.unq.n.log D.terms.post.stem.n.log 9.775000
## D.chrs.uppr.n.log D.ratio.wrds.stop.n.wrds.n 22.941176
## D.ratio.weight.sum.wrds.n <NA> 78.200000
## D.T.fulli <NA> 88.000000
## .clusterid <NA> 5.428571
## .clusterid.fctr <NA> 5.428571
## UniqueID <NA> 1.000000
## D.T.function. <NA> 55.090909
## startprice.dcm1.is9 startprice.dcm2.is9 1.562249
## startprice.dcm2.is9 <NA> 1.786026
## D.P.gold <NA> 0.000000
## D.P.http <NA> 0.000000
## D.chrs.pnct02.n.log <NA> 0.000000
## D.chrs.pnct04.n.log <NA> 0.000000
## D.chrs.pnct09.n.log <NA> 0.000000
## D.chrs.pnct10.n.log <NA> 0.000000
## D.chrs.pnct17.n.log <NA> 0.000000
## D.chrs.pnct18.n.log <NA> 0.000000
## D.chrs.pnct19.n.log <NA> 0.000000
## D.chrs.pnct20.n.log <NA> 0.000000
## D.chrs.pnct21.n.log <NA> 0.000000
## D.chrs.pnct22.n.log <NA> 0.000000
## D.chrs.pnct23.n.log <NA> 0.000000
## D.chrs.pnct24.n.log <NA> 0.000000
## D.chrs.pnct25.n.log <NA> 0.000000
## D.chrs.pnct26.n.log <NA> 0.000000
## D.chrs.pnct27.n.log <NA> 0.000000
## D.chrs.pnct28.n.log <NA> 0.000000
## D.chrs.pnct29.n.log <NA> 0.000000
## D.chrs.pnct30.n.log <NA> 0.000000
## biddable <NA> 0.000000
## sold <NA> 0.000000
## startprice.dgt3.is9 <NA> 0.000000
## percentUnique zeroVar nzv is.cor.y.abs.low
## startprice.log10.cut.fctr 0.4702194 FALSE FALSE FALSE
## startprice 24.1379310 FALSE FALSE FALSE
## startprice.dgt1.is9 0.3134796 FALSE FALSE FALSE
## startprice.dgt2.is9 0.3134796 FALSE FALSE FALSE
## D.ratio.wrds.stop.n.wrds.n 10.0313480 FALSE FALSE FALSE
## prdl.descr.my.fctr 3.1347962 FALSE FALSE FALSE
## D.weight.sum.stem.stop.Ratio 25.7053292 FALSE FALSE FALSE
## D.T.mint 1.2539185 FALSE TRUE FALSE
## D.chrs.pnct15.n.log 0.3134796 FALSE TRUE FALSE
## D.T.use 2.3510972 FALSE TRUE FALSE
## D.T.like 1.4106583 FALSE TRUE FALSE
## D.chrs.pnct01.n.log 0.6269592 FALSE TRUE FALSE
## storage.fctr 0.7836991 FALSE FALSE FALSE
## .rnorm 100.0000000 FALSE FALSE FALSE
## D.T.new 1.5673981 FALSE TRUE TRUE
## D.terms.n.stem.stop.Ratio 0.9404389 FALSE TRUE TRUE
## D.T.lock 1.2539185 FALSE TRUE TRUE
## D.P.air 0.3134796 FALSE TRUE TRUE
## D.T.work 2.0376176 FALSE TRUE TRUE
## D.chrs.pnct06.n.log 0.4702194 FALSE TRUE TRUE
## D.P.spacegray 0.3134796 FALSE TRUE TRUE
## D.T.scratch 2.1943574 FALSE TRUE TRUE
## D.chrs.pnct08.n.log 0.4702194 FALSE TRUE TRUE
## D.T.perfect 0.9404389 FALSE TRUE TRUE
## D.chrs.pnct16.n.log 0.4702194 FALSE TRUE TRUE
## D.T.icloud 1.4106583 FALSE TRUE TRUE
## color.fctr 0.7836991 FALSE FALSE TRUE
## D.chrs.pnct03.n.log 0.4702194 FALSE TRUE TRUE
## D.P.black 0.3134796 FALSE TRUE TRUE
## D.dgts.n.log 1.5673981 FALSE TRUE TRUE
## D.P.white 0.4702194 FALSE TRUE TRUE
## D.T.excel 1.7241379 FALSE TRUE FALSE
## D.T.see 1.4106583 FALSE TRUE FALSE
## D.T.item 1.7241379 FALSE TRUE FALSE
## carrier.fctr 1.0971787 FALSE FALSE FALSE
## D.T.great 1.7241379 FALSE TRUE FALSE
## D.P.mini 0.3134796 FALSE TRUE FALSE
## condition.fctr 0.9404389 FALSE FALSE FALSE
## D.chrs.pnct14.n.log 0.3134796 FALSE TRUE FALSE
## D.chrs.pnct07.n.log 0.3134796 FALSE TRUE FALSE
## D.T.crack 1.5673981 FALSE TRUE FALSE
## D.chrs.pnct12.n.log 0.4702194 FALSE TRUE FALSE
## cellular.fctr 0.4702194 FALSE FALSE FALSE
## D.T.condit 1.8808777 FALSE TRUE FALSE
## D.chrs.pnct05.n.log 0.3134796 FALSE TRUE FALSE
## D.T.no 2.0376176 FALSE TRUE FALSE
## D.chrs.pnct11.n.log 0.7836991 FALSE FALSE FALSE
## D.T.good 2.0376176 FALSE TRUE FALSE
## D.wrds.stop.n.log 2.6645768 FALSE FALSE FALSE
## D.chrs.pnct13.n.log 0.9404389 FALSE FALSE FALSE
## D.T.screen 2.3510972 FALSE TRUE FALSE
## D.T.box 1.5673981 FALSE TRUE FALSE
## D.wrds.n.log 3.7617555 FALSE FALSE FALSE
## D.terms.post.stop.n 2.1943574 FALSE FALSE FALSE
## D.terms.post.stem.n 2.1943574 FALSE FALSE FALSE
## D.chrs.n.log 13.1661442 FALSE FALSE FALSE
## D.weight.post.stop.sum 34.6394984 FALSE FALSE FALSE
## D.terms.post.stop.n.log 2.1943574 FALSE FALSE FALSE
## D.weight.post.stem.sum 34.6394984 FALSE FALSE FALSE
## D.weight.sum 34.6394984 FALSE FALSE FALSE
## D.terms.post.stem.n.log 2.1943574 FALSE FALSE FALSE
## D.wrds.unq.n.log 2.1943574 FALSE FALSE FALSE
## D.chrs.uppr.n.log 11.2852665 FALSE FALSE FALSE
## D.ratio.weight.sum.wrds.n 35.4231975 FALSE FALSE FALSE
## D.T.fulli 1.4106583 FALSE TRUE FALSE
## .clusterid 0.7836991 FALSE FALSE FALSE
## .clusterid.fctr 0.7836991 FALSE FALSE FALSE
## UniqueID 100.0000000 FALSE FALSE FALSE
## D.T.function. 1.5673981 FALSE TRUE FALSE
## startprice.dcm1.is9 0.3134796 FALSE FALSE FALSE
## startprice.dcm2.is9 0.3134796 FALSE FALSE FALSE
## D.P.gold 0.1567398 TRUE TRUE NA
## D.P.http 0.1567398 TRUE TRUE NA
## D.chrs.pnct02.n.log 0.1567398 TRUE TRUE NA
## D.chrs.pnct04.n.log 0.1567398 TRUE TRUE NA
## D.chrs.pnct09.n.log 0.1567398 TRUE TRUE NA
## D.chrs.pnct10.n.log 0.1567398 TRUE TRUE NA
## D.chrs.pnct17.n.log 0.1567398 TRUE TRUE NA
## D.chrs.pnct18.n.log 0.1567398 TRUE TRUE NA
## D.chrs.pnct19.n.log 0.1567398 TRUE TRUE NA
## D.chrs.pnct20.n.log 0.1567398 TRUE TRUE NA
## D.chrs.pnct21.n.log 0.1567398 TRUE TRUE NA
## D.chrs.pnct22.n.log 0.1567398 TRUE TRUE NA
## D.chrs.pnct23.n.log 0.1567398 TRUE TRUE NA
## D.chrs.pnct24.n.log 0.1567398 TRUE TRUE NA
## D.chrs.pnct25.n.log 0.1567398 TRUE TRUE NA
## D.chrs.pnct26.n.log 0.1567398 TRUE TRUE NA
## D.chrs.pnct27.n.log 0.1567398 TRUE TRUE NA
## D.chrs.pnct28.n.log 0.1567398 TRUE TRUE NA
## D.chrs.pnct29.n.log 0.1567398 TRUE TRUE NA
## D.chrs.pnct30.n.log 0.1567398 TRUE TRUE NA
## biddable 0.1567398 TRUE TRUE NA
## sold 0.1567398 TRUE TRUE NA
## startprice.dgt3.is9 0.1567398 TRUE TRUE NAplt_feats_df <- glb_feats_df
print(myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
colorcol_name="nzv", jitter=TRUE) +
#geom_point(aes(shape=nzv)) +
geom_point() +
xlim(-5, 25) +
geom_hline(yintercept=glb_nzv_freqCut) +
geom_vline(xintercept=glb_nzv_uniqueCut))## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor## Warning: Removed 7 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_point).## Warning: Removed 7 rows containing missing values (geom_point).
print(subset(glb_feats_df, nzv))## id cor.y
## D.T.mint D.T.mint 6.497510e-02
## D.chrs.pnct15.n.log D.chrs.pnct15.n.log 5.993738e-02
## D.T.use D.T.use 4.704955e-02
## D.T.like D.T.like 3.708720e-02
## D.chrs.pnct01.n.log D.chrs.pnct01.n.log 3.449116e-02
## D.T.new D.T.new 1.938403e-02
## D.terms.n.stem.stop.Ratio D.terms.n.stem.stop.Ratio 1.480705e-02
## D.T.lock D.T.lock 1.259657e-02
## D.P.air D.P.air 1.121413e-02
## D.T.work D.T.work 1.082156e-02
## D.chrs.pnct06.n.log D.chrs.pnct06.n.log 1.029894e-02
## D.P.spacegray D.P.spacegray 8.461024e-03
## D.T.scratch D.T.scratch 6.098563e-03
## D.chrs.pnct08.n.log D.chrs.pnct08.n.log 7.742276e-05
## D.T.perfect D.T.perfect -2.582106e-03
## D.chrs.pnct16.n.log D.chrs.pnct16.n.log -3.734420e-03
## D.T.icloud D.T.icloud -7.105792e-03
## D.chrs.pnct03.n.log D.chrs.pnct03.n.log -1.902309e-02
## D.P.black D.P.black -2.110770e-02
## D.dgts.n.log D.dgts.n.log -2.815344e-02
## D.P.white D.P.white -2.823774e-02
## D.T.excel D.T.excel -2.909137e-02
## D.T.see D.T.see -3.616379e-02
## D.T.item D.T.item -3.660548e-02
## D.T.great D.T.great -4.080366e-02
## D.P.mini D.P.mini -4.179600e-02
## D.chrs.pnct14.n.log D.chrs.pnct14.n.log -4.836149e-02
## D.chrs.pnct07.n.log D.chrs.pnct07.n.log -4.994372e-02
## D.T.crack D.T.crack -5.082401e-02
## D.chrs.pnct12.n.log D.chrs.pnct12.n.log -5.639687e-02
## D.T.condit D.T.condit -6.648443e-02
## D.chrs.pnct05.n.log D.chrs.pnct05.n.log -7.068660e-02
## D.T.no D.T.no -7.226042e-02
## D.T.good D.T.good -9.655397e-02
## D.T.screen D.T.screen -1.068446e-01
## D.T.box D.T.box -1.086714e-01
## D.T.fulli D.T.fulli -1.802946e-01
## D.T.function. D.T.function. -2.207568e-01
## D.P.gold D.P.gold NA
## D.P.http D.P.http NA
## D.chrs.pnct02.n.log D.chrs.pnct02.n.log NA
## D.chrs.pnct04.n.log D.chrs.pnct04.n.log NA
## D.chrs.pnct09.n.log D.chrs.pnct09.n.log NA
## D.chrs.pnct10.n.log D.chrs.pnct10.n.log NA
## D.chrs.pnct17.n.log D.chrs.pnct17.n.log NA
## D.chrs.pnct18.n.log D.chrs.pnct18.n.log NA
## D.chrs.pnct19.n.log D.chrs.pnct19.n.log NA
## D.chrs.pnct20.n.log D.chrs.pnct20.n.log NA
## D.chrs.pnct21.n.log D.chrs.pnct21.n.log NA
## D.chrs.pnct22.n.log D.chrs.pnct22.n.log NA
## D.chrs.pnct23.n.log D.chrs.pnct23.n.log NA
## D.chrs.pnct24.n.log D.chrs.pnct24.n.log NA
## D.chrs.pnct25.n.log D.chrs.pnct25.n.log NA
## D.chrs.pnct26.n.log D.chrs.pnct26.n.log NA
## D.chrs.pnct27.n.log D.chrs.pnct27.n.log NA
## D.chrs.pnct28.n.log D.chrs.pnct28.n.log NA
## D.chrs.pnct29.n.log D.chrs.pnct29.n.log NA
## D.chrs.pnct30.n.log D.chrs.pnct30.n.log NA
## biddable biddable NA
## sold sold NA
## startprice.dgt3.is9 startprice.dgt3.is9 NA
## exclude.as.feat cor.y.abs cor.high.X
## D.T.mint 1 6.497510e-02 <NA>
## D.chrs.pnct15.n.log 0 5.993738e-02 <NA>
## D.T.use 1 4.704955e-02 <NA>
## D.T.like 1 3.708720e-02 <NA>
## D.chrs.pnct01.n.log 0 3.449116e-02 <NA>
## D.T.new 1 1.938403e-02 <NA>
## D.terms.n.stem.stop.Ratio 0 1.480705e-02 <NA>
## D.T.lock 1 1.259657e-02 <NA>
## D.P.air 1 1.121413e-02 <NA>
## D.T.work 1 1.082156e-02 <NA>
## D.chrs.pnct06.n.log 0 1.029894e-02 <NA>
## D.P.spacegray 1 8.461024e-03 <NA>
## D.T.scratch 1 6.098563e-03 <NA>
## D.chrs.pnct08.n.log 0 7.742276e-05 <NA>
## D.T.perfect 1 2.582106e-03 <NA>
## D.chrs.pnct16.n.log 0 3.734420e-03 <NA>
## D.T.icloud 1 7.105792e-03 <NA>
## D.chrs.pnct03.n.log 0 1.902309e-02 <NA>
## D.P.black 1 2.110770e-02 <NA>
## D.dgts.n.log 0 2.815344e-02 <NA>
## D.P.white 1 2.823774e-02 <NA>
## D.T.excel 1 2.909137e-02 <NA>
## D.T.see 1 3.616379e-02 <NA>
## D.T.item 1 3.660548e-02 <NA>
## D.T.great 1 4.080366e-02 <NA>
## D.P.mini 1 4.179600e-02 <NA>
## D.chrs.pnct14.n.log 0 4.836149e-02 <NA>
## D.chrs.pnct07.n.log 0 4.994372e-02 <NA>
## D.T.crack 1 5.082401e-02 <NA>
## D.chrs.pnct12.n.log 0 5.639687e-02 <NA>
## D.T.condit 1 6.648443e-02 <NA>
## D.chrs.pnct05.n.log 0 7.068660e-02 <NA>
## D.T.no 1 7.226042e-02 <NA>
## D.T.good 1 9.655397e-02 <NA>
## D.T.screen 1 1.068446e-01 <NA>
## D.T.box 1 1.086714e-01 <NA>
## D.T.fulli 1 1.802946e-01 <NA>
## D.T.function. 1 2.207568e-01 <NA>
## D.P.gold 1 NA <NA>
## D.P.http 1 NA <NA>
## D.chrs.pnct02.n.log 0 NA <NA>
## D.chrs.pnct04.n.log 0 NA <NA>
## D.chrs.pnct09.n.log 0 NA <NA>
## D.chrs.pnct10.n.log 0 NA <NA>
## D.chrs.pnct17.n.log 0 NA <NA>
## D.chrs.pnct18.n.log 0 NA <NA>
## D.chrs.pnct19.n.log 0 NA <NA>
## D.chrs.pnct20.n.log 0 NA <NA>
## D.chrs.pnct21.n.log 0 NA <NA>
## D.chrs.pnct22.n.log 0 NA <NA>
## D.chrs.pnct23.n.log 0 NA <NA>
## D.chrs.pnct24.n.log 0 NA <NA>
## D.chrs.pnct25.n.log 0 NA <NA>
## D.chrs.pnct26.n.log 0 NA <NA>
## D.chrs.pnct27.n.log 0 NA <NA>
## D.chrs.pnct28.n.log 0 NA <NA>
## D.chrs.pnct29.n.log 0 NA <NA>
## D.chrs.pnct30.n.log 0 NA <NA>
## biddable 0 NA <NA>
## sold 1 NA <NA>
## startprice.dgt3.is9 0 NA <NA>
## freqRatio percentUnique zeroVar nzv
## D.T.mint 315.00000 1.2539185 FALSE TRUE
## D.chrs.pnct15.n.log 158.50000 0.3134796 FALSE TRUE
## D.T.use 52.09091 2.3510972 FALSE TRUE
## D.T.like 314.50000 1.4106583 FALSE TRUE
## D.chrs.pnct01.n.log 40.93333 0.6269592 FALSE TRUE
## D.T.new 102.00000 1.5673981 FALSE TRUE
## D.terms.n.stem.stop.Ratio 104.33333 0.9404389 FALSE TRUE
## D.T.lock 208.66667 1.2539185 FALSE TRUE
## D.P.air 105.33333 0.3134796 FALSE TRUE
## D.T.work 85.14286 2.0376176 FALSE TRUE
## D.chrs.pnct06.n.log 51.50000 0.4702194 FALSE TRUE
## D.P.spacegray 318.00000 0.3134796 FALSE TRUE
## D.T.scratch 57.90000 2.1943574 FALSE TRUE
## D.chrs.pnct08.n.log 69.66667 0.4702194 FALSE TRUE
## D.T.perfect 157.00000 0.9404389 FALSE TRUE
## D.chrs.pnct16.n.log 51.41667 0.4702194 FALSE TRUE
## D.T.icloud 313.50000 1.4106583 FALSE TRUE
## D.chrs.pnct03.n.log 69.33333 0.4702194 FALSE TRUE
## D.P.black 158.50000 0.3134796 FALSE TRUE
## D.dgts.n.log 50.08333 1.5673981 FALSE TRUE
## D.P.white 211.33333 0.4702194 FALSE TRUE
## D.T.excel 154.00000 1.7241379 FALSE TRUE
## D.T.see 208.00000 1.4106583 FALSE TRUE
## D.T.item 87.57143 1.7241379 FALSE TRUE
## D.T.great 122.40000 1.7241379 FALSE TRUE
## D.P.mini 105.33333 0.3134796 FALSE TRUE
## D.chrs.pnct14.n.log 78.75000 0.3134796 FALSE TRUE
## D.chrs.pnct07.n.log 637.00000 0.3134796 FALSE TRUE
## D.T.crack 123.40000 1.5673981 FALSE TRUE
## D.chrs.pnct12.n.log 29.23810 0.4702194 FALSE TRUE
## D.T.condit 37.26667 1.8808777 FALSE TRUE
## D.chrs.pnct05.n.log 318.00000 0.3134796 FALSE TRUE
## D.T.no 45.76923 2.0376176 FALSE TRUE
## D.T.good 85.71429 2.0376176 FALSE TRUE
## D.T.screen 52.72727 2.3510972 FALSE TRUE
## D.T.box 124.00000 1.5673981 FALSE TRUE
## D.T.fulli 88.00000 1.4106583 FALSE TRUE
## D.T.function. 55.09091 1.5673981 FALSE TRUE
## D.P.gold 0.00000 0.1567398 TRUE TRUE
## D.P.http 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct02.n.log 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct04.n.log 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct09.n.log 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct10.n.log 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct17.n.log 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct18.n.log 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct19.n.log 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct20.n.log 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct21.n.log 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct22.n.log 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct23.n.log 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct24.n.log 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct25.n.log 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct26.n.log 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct27.n.log 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct28.n.log 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct29.n.log 0.00000 0.1567398 TRUE TRUE
## D.chrs.pnct30.n.log 0.00000 0.1567398 TRUE TRUE
## biddable 0.00000 0.1567398 TRUE TRUE
## sold 0.00000 0.1567398 TRUE TRUE
## startprice.dgt3.is9 0.00000 0.1567398 TRUE TRUE
## is.cor.y.abs.low
## D.T.mint FALSE
## D.chrs.pnct15.n.log FALSE
## D.T.use FALSE
## D.T.like FALSE
## D.chrs.pnct01.n.log FALSE
## D.T.new TRUE
## D.terms.n.stem.stop.Ratio TRUE
## D.T.lock TRUE
## D.P.air TRUE
## D.T.work TRUE
## D.chrs.pnct06.n.log TRUE
## D.P.spacegray TRUE
## D.T.scratch TRUE
## D.chrs.pnct08.n.log TRUE
## D.T.perfect TRUE
## D.chrs.pnct16.n.log TRUE
## D.T.icloud TRUE
## D.chrs.pnct03.n.log TRUE
## D.P.black TRUE
## D.dgts.n.log TRUE
## D.P.white TRUE
## D.T.excel FALSE
## D.T.see FALSE
## D.T.item FALSE
## D.T.great FALSE
## D.P.mini FALSE
## D.chrs.pnct14.n.log FALSE
## D.chrs.pnct07.n.log FALSE
## D.T.crack FALSE
## D.chrs.pnct12.n.log FALSE
## D.T.condit FALSE
## D.chrs.pnct05.n.log FALSE
## D.T.no FALSE
## D.T.good FALSE
## D.T.screen FALSE
## D.T.box FALSE
## D.T.fulli FALSE
## D.T.function. FALSE
## D.P.gold NA
## D.P.http NA
## D.chrs.pnct02.n.log NA
## D.chrs.pnct04.n.log NA
## D.chrs.pnct09.n.log NA
## D.chrs.pnct10.n.log NA
## D.chrs.pnct17.n.log NA
## D.chrs.pnct18.n.log NA
## D.chrs.pnct19.n.log NA
## D.chrs.pnct20.n.log NA
## D.chrs.pnct21.n.log NA
## D.chrs.pnct22.n.log NA
## D.chrs.pnct23.n.log NA
## D.chrs.pnct24.n.log NA
## D.chrs.pnct25.n.log NA
## D.chrs.pnct26.n.log NA
## D.chrs.pnct27.n.log NA
## D.chrs.pnct28.n.log NA
## D.chrs.pnct29.n.log NA
## D.chrs.pnct30.n.log NA
## biddable NA
## sold NA
## startprice.dgt3.is9 NAtmp_allobs_df <-
glb_allobs_df[, union(setdiff(names(glb_allobs_df), subset(glb_feats_df, nzv)$id),
glb_cluster_entropy_var)]
glb_trnobs_df <- subset(tmp_allobs_df, .src == "Train")
glb_newobs_df <- subset(tmp_allobs_df, .src == "Test")
glb_feats_df$interaction.feat <- NA
for (feat in names(glb_interaction_only_feats_lst))
glb_feats_df[glb_feats_df$id %in% feat, "interaction.feat"] <-
glb_interaction_only_feats_lst[[feat]]
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df
indep_vars <- subset(glb_feats_df, !nzv & (exclude.as.feat != 1))[, "id"]
numeric_indep_vars <- indep_vars[!grepl(".fctr", indep_vars, fixed=TRUE)]
glb_feats_df$shapiro.test.p.value <- NA
glb_feats_df[glb_feats_df$id %in% numeric_indep_vars, "shapiro.test.p.value"] <-
sapply(numeric_indep_vars, function(var) shapiro.test(glb_trnobs_df[, var])$p.value)
not_nrml_feats_df <- glb_feats_df %>%
subset(!is.na(shapiro.test.p.value)) %>%
subset((shapiro.test.p.value < 0.05) || (id == ".rnorm")) %>%
arrange(shapiro.test.p.value)
row.names(not_nrml_feats_df) <- not_nrml_feats_df$id
#plt_trnobs_df <- glb_trnobs_df[, c("D.npnct05.log", ".rnorm")]
plt_trnobs_df <- glb_trnobs_df[, c(union(not_nrml_feats_df$id[1:min(5, nrow(not_nrml_feats_df))],
".rnorm"), glb_cluster_entropy_var)]
print(myplot_violin(plt_trnobs_df, setdiff(names(plt_trnobs_df), glb_cluster_entropy_var),
xcol_name = glb_cluster_entropy_var) +
facet_wrap(~variable, scales="free"))## Warning in myplot_violin(plt_trnobs_df, setdiff(names(plt_trnobs_df),
## glb_cluster_entropy_var), : xcol_name:sold is not a factor; creating
## sold_fctr
#myplot_histogram(plt_trnobs_df, "D.npnct11.log", fill_col_name="sold", show_stats = TRUE)
myadjust_interaction_feats <- function(vars_vctr) {
for (feat in subset(glb_feats_df, !is.na(interaction.feat))$id)
if (feat %in% vars_vctr)
vars_vctr <- union(setdiff(vars_vctr, feat),
paste0(glb_feats_df[glb_feats_df$id == feat, "interaction.feat"], ":",
feat))
return(vars_vctr)
}
# shd .clusterid.fctr be excluded from this ? or include encoding of glb_category_var:.clusterid.fctr ?
indep_vars <-
myadjust_interaction_feats(subset(glb_feats_df, !nzv & (exclude.as.feat != 1))[, "id"])
myrun_rfe <- function(obs_df, indep_vars, sizes=NULL) {
rfe_obs_df <- myget_vectorized_obs_df(obs_df, glb_rsp_var, indep_vars)
predictors_vctr <- setdiff(names(rfe_obs_df), glb_rsp_var)
if (is.null(sizes))
sizes <- tail(2^(1:as.integer(log2(length(predictors_vctr)))), 5)
rfe_control <- rfeControl(functions=rfFuncs, method="repeatedcv", number=glb_rcv_n_folds,
repeats=glb_rcv_n_repeats, verbose=TRUE, returnResamp = "all",
seeds=mygen_seeds(seeds_lst_len=(glb_rcv_n_folds * glb_rcv_n_repeats) + 1,
seeds_elmnt_lst_len=(length(sizes) + 1)))
set.seed(113)
rfe_results <- rfe(rfe_obs_df[, predictors_vctr],
rfe_obs_df[, glb_rsp_var],
sizes=sizes, metric=unlist(strsplit(glb_model_evl_criteria, "[.]"))[2],
maximize=ifelse(unlist(strsplit(glb_model_evl_criteria, "[.]"))[1] == "max",
TRUE, FALSE),
rfeControl=rfe_control)
print(rfe_results)
print(predictors(rfe_results))
# print(plot(rfe_results, type=c("g", "o")))
# print(plot(rfe_results))
print(ggplot(rfe_results))
return(rfe_results)
}
rfe_fit_results <- myrun_rfe(glb_fitobs_df, indep_vars, glb_rfe_fit_sizes)##
## Recursive feature selection
##
## Outer resampling method: Cross-Validated (3 fold, repeated 3 times)
##
## Resampling performance over subset size:
##
## Variables RMSE Rsquared RMSESD RsquaredSD Selected
## 8 0.5348 0.6849 0.06161 0.07386
## 11 0.5039 0.7116 0.06648 0.07582
## 16 0.4991 0.7163 0.05284 0.05651 *
## 21 0.5022 0.7126 0.05524 0.05966
## 32 0.5091 0.7052 0.05669 0.06192
## 64 0.5134 0.7003 0.05354 0.05949
## 128 0.5137 0.7000 0.05232 0.05783
## 153 0.5081 0.7059 0.05352 0.05816
##
## The top 5 variables (out of 16):
## startprice.dgt1.is9, startprice.dcm2.is9, startprice.dcm1.is9, startprice.dgt2.is9, condition.fctrFor parts or not working
##
## [1] "startprice.dgt1.is9"
## [2] "startprice.dcm2.is9"
## [3] "startprice.dcm1.is9"
## [4] "startprice.dgt2.is9"
## [5] "condition.fctrFor parts or not working"
## [6] "prdl.descr.my.fctriPad1#0"
## [7] "prdl.descr.my.fctriPadAir2#0"
## [8] "color.fctrSpace Gray"
## [9] "condition.fctrNew"
## [10] "D.ratio.weight.sum.wrds.n"
## [11] "color.fctrUnknown"
## [12] "prdl.descr.my.fctrUnknown#0"
## [13] "prdl.descr.my.fctriPad1#1"
## [14] "condition.fctrManufacturer refurbished"
## [15] "cellular.fctr1:carrier.fctrSprint"
## [16] "prdl.descr.my.fctriPad2#0"
# print(all.equal(rfe_results[-which(names(rfe_results) == "times")],
# sav_rfe_results[-which(names(sav_rfe_results) == "times")]))
# require(mRMRe)
# indep_vars_vctr <- subset(glb_feats_df, !nzv &
# (exclude.as.feat != 1))[, "id"]
# indep_vars_vctr <- setdiff(indep_vars_vctr,
# myfind_fctr_cols_df(glb_trnobs_df[, c(glb_rsp_var, indep_vars_vctr)]))
# tmp_trnobs_df <- glb_trnobs_df[, c(glb_rsp_var, indep_vars_vctr)]
# tmp_trnobs_df$biddable <- as.numeric(tmp_trnobs_df$biddable)
# dd <- mRMR.data(data = tmp_trnobs_df)
# mRMRe.fltr <- mRMR.classic(data = dd, target_indices = c(1), feature_count = 10)
# print(solutions(mRMRe.fltr)[[1]])
# print(apply(solutions(mRMRe.fltr)[[1]], 2, function(x, y) { return(y[x]) },
# y=featureNames(dd)))
# print(featureNames(dd)[solutions(mRMRe.fltr)[[1]]])
# print(mRMRe.fltr@filters); print(mRMRe.fltr@scores)
mycheck_problem_data(glb_allobs_df, terminate = TRUE)## [1] "numeric data missing in : "
## sold
## 376
## [1] "numeric data w/ 0s in : "
## sold startprice.log10
## 197 31
## cellular.fctr D.terms.post.stop.n
## 789 754
## D.terms.post.stop.n.log D.weight.post.stop.sum
## 754 754
## D.terms.post.stem.n D.terms.post.stem.n.log
## 754 754
## D.weight.post.stem.sum D.T.condit
## 754 1049
## D.T.use D.T.new
## 1082 1157
## D.T.screen D.T.scratch
## 1125 1106
## D.T.good D.T.great
## 1145 1157
## D.T.excel D.T.work
## 1164 1138
## D.T.no D.T.function.
## 1135 1155
## D.T.like D.T.crack
## 1186 1174
## D.T.fulli D.T.box
## 1175 1177
## D.T.mint D.T.item
## 1192 1167
## D.T.lock D.T.see
## 1186 1181
## D.T.icloud D.T.perfect
## 1191 1193
## D.wrds.n.log D.wrds.unq.n.log
## 753 754
## D.weight.sum D.ratio.weight.sum.wrds.n
## 754 754
## D.chrs.n.log D.chrs.uppr.n.log
## 753 753
## D.dgts.n.log D.chrs.pnct01.n.log
## 1139 1173
## D.chrs.pnct02.n.log D.chrs.pnct03.n.log
## 1211 1188
## D.chrs.pnct04.n.log D.chrs.pnct05.n.log
## 1211 1204
## D.chrs.pnct06.n.log D.chrs.pnct07.n.log
## 1178 1210
## D.chrs.pnct08.n.log D.chrs.pnct09.n.log
## 1194 1210
## D.chrs.pnct10.n.log D.chrs.pnct11.n.log
## 1211 1059
## D.chrs.pnct12.n.log D.chrs.pnct13.n.log
## 1165 919
## D.chrs.pnct14.n.log D.chrs.pnct15.n.log
## 1191 1205
## D.chrs.pnct16.n.log D.chrs.pnct17.n.log
## 1175 1211
## D.chrs.pnct18.n.log D.chrs.pnct19.n.log
## 1211 1211
## D.chrs.pnct20.n.log D.chrs.pnct21.n.log
## 1211 1211
## D.chrs.pnct22.n.log D.chrs.pnct23.n.log
## 1211 1211
## D.chrs.pnct24.n.log D.chrs.pnct25.n.log
## 1211 1211
## D.chrs.pnct26.n.log D.chrs.pnct27.n.log
## 1211 1211
## D.chrs.pnct28.n.log D.chrs.pnct29.n.log
## 1211 1211
## D.chrs.pnct30.n.log D.wrds.stop.n.log
## 1211 822
## D.P.http D.P.mini
## 1211 1197
## D.P.air D.P.black
## 1202 1199
## D.P.white D.P.gold
## 1206 1211
## D.P.spacegray startprice.dgt1.is9
## 1207 908
## startprice.dgt2.is9 startprice.dgt3.is9
## 1064 1211
## startprice.dcm1.is9 startprice.dcm2.is9
## 775 830
## [1] "numeric data w/ Infs in : "
## named integer(0)
## [1] "numeric data w/ NaNs in : "
## named integer(0)
## [1] "string data missing in : "
## description condition cellular carrier color storage
## 753 0 0 0 0 0
## productline .grpid descr.my .lcn
## 0 NA 753 0# glb_allobs_df %>% filter(is.na(Married.fctr)) %>% tbl_df()
# glb_allobs_df %>% count(Married.fctr)
# levels(glb_allobs_df$Married.fctr)
print("glb_feats_df:"); print(dim(glb_feats_df))## [1] "glb_feats_df:"## [1] 94 12sav_feats_df <- glb_feats_df
glb_feats_df <- sav_feats_df
glb_feats_df[, "rsp_var_raw"] <- FALSE
glb_feats_df[glb_feats_df$id == glb_rsp_var_raw, "rsp_var_raw"] <- TRUE
glb_feats_df$exclude.as.feat <- (glb_feats_df$exclude.as.feat == 1)
if (!is.null(glb_id_var) && glb_id_var != ".rownames")
glb_feats_df[glb_feats_df$id %in% glb_id_var, "id_var"] <- TRUE
add_feats_df <- data.frame(id=glb_rsp_var, exclude.as.feat=TRUE, rsp_var=TRUE)
row.names(add_feats_df) <- add_feats_df$id; print(add_feats_df)## id exclude.as.feat rsp_var
## startprice.log10 startprice.log10 TRUE TRUEglb_feats_df <- myrbind_df(glb_feats_df, add_feats_df)
if (glb_id_var != ".rownames")
print(subset(glb_feats_df, rsp_var_raw | rsp_var | id_var)) else
print(subset(glb_feats_df, rsp_var_raw | rsp_var)) ## id cor.y exclude.as.feat cor.y.abs
## startprice startprice 0.7113445 TRUE 0.7113445
## UniqueID UniqueID -0.1959774 TRUE 0.1959774
## startprice.log10 startprice.log10 NA TRUE NA
## cor.high.X freqRatio percentUnique zeroVar nzv
## startprice <NA> 4 24.13793 FALSE FALSE
## UniqueID <NA> 1 100.00000 FALSE FALSE
## startprice.log10 <NA> NA NA NA NA
## is.cor.y.abs.low interaction.feat shapiro.test.p.value
## startprice FALSE <NA> NA
## UniqueID FALSE <NA> NA
## startprice.log10 NA <NA> NA
## rsp_var_raw id_var rsp_var
## startprice TRUE NA NA
## UniqueID FALSE TRUE NA
## startprice.log10 NA NA TRUEprint("glb_feats_df vs. glb_allobs_df: "); ## [1] "glb_feats_df vs. glb_allobs_df: "print(setdiff(glb_feats_df$id, names(glb_allobs_df)))## character(0)print("glb_allobs_df vs. glb_feats_df: "); ## [1] "glb_allobs_df vs. glb_feats_df: "# Ensure these are only chr vars
print(setdiff(setdiff(names(glb_allobs_df), glb_feats_df$id),
myfind_chr_cols_df(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
file=paste0(glb_out_pfx, "selfts_dsk.RData"))
# load(paste0(glb_out_pfx, "blddfs_dsk.RData"))
# if (!all.equal(tmp_feats_df, glb_feats_df))
# stop("glb_feats_df r/w not working")
# if (!all.equal(tmp_entity_df, glb_allobs_df))
# stop("glb_allobs_df r/w not working")
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 9 select.features 6 0 0 81.578 117.869
## 10 fit.models 7 0 0 117.870 NA
## elapsed
## 9 36.291
## 10 NAStep 7.0: fit models
# load(paste0(glb_out_pfx, "dsk.RData"))
get_model_sel_frmla <- function() {
model_evl_terms <- c(NULL)
for (metric in glb_model_evl_criteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
return(model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse=" ")))
}
get_dsp_models_df <- function() {
dsp_models_df <-
orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glb_model_evl_criteria)]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl)
nrow(subset(modelLookup(mdl$method), parameter != "parameter")))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glb_out_pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
return(dsp_models_df)
}
#get_dsp_models_df()
if (glb_is_classification && glb_is_binomial &&
(length(unique(glb_fitobs_df[, glb_rsp_var])) < 2))
stop("glb_fitobs_df$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glb_fitobs_df[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
# while(length(max_cor_y_x_vars) < 2) {
# max_cor_y_x_vars <- c(max_cor_y_x_vars, orderBy(~ -cor.y.abs,
# subset(glb_feats_df, (exclude.as.feat == 0) & !is.cor.y.abs.low))[3, "id"])
# }
#stop(here"); glb_to_sav(); glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
c("id.prefix", "method", "type",
# trainControl params
"preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# train params
"metric", "metric.maximize", "tune.df")## [1] "id.prefix" "method" "type"
## [4] "preProc.method" "cv.n.folds" "cv.n.repeats"
## [7] "summary.fn" "metric" "metric.maximize"
## [10] "tune.df"# Baseline
if (!is.null(glb_Baseline_mdl_var))
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indep_vars_vctr=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="MFO", type=glb_model_type, trainControl.method="none",
train.method=ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indep_vars=".rnorm", rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: MFO.lm"
## [1] " indep_vars: .rnorm"
## Fitting parameter = none on full training set
##
## Call:
## lm(formula = .outcome ~ ., data = dat)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4151 -1.3470 0.3799 0.6733 1.3747
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.42713 0.03766 37.895 <2e-16 ***
## .rnorm 0.04089 0.03748 1.091 0.276
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.938 on 619 degrees of freedom
## Multiple R-squared: 0.001919, Adjusted R-squared: 0.0003065
## F-statistic: 1.19 on 1 and 619 DF, p-value: 0.2757
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO.lm .rnorm 0 0.538
## min.elapsedtime.final max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit
## 1 0.004 0.001918896 0.9364496 0.0003064874
## max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.OOB
## 1 1.783336e-05 1.161981 -0.001682817if (glb_is_classification)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
ret_lst <- myfit_mdl(mdl_id="Random", model_method="myrandom_classfr",
model_type=glb_model_type,
indep_vars_vctr=".rnorm",
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
# Any models that have tuning parameters has "better" results with cross-validation
# (except bag & rf) & "different" results for different outcome metrics
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Max.cor.Y.rcv.1X1", type=glb_model_type, trainControl.method="none",
train.method="glmnet")),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.rcv.1X1.glmnet"
## [1] " indep_vars: startprice.dcm2.is9,startprice.dgt1.is9"## Loading required package: glmnet
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
##
## The following object is masked from 'package:tidyr':
##
## expand
##
## Loaded glmnet 2.0-2## Fitting alpha = 0.1, lambda = 0.0113 on full training set
## Length Class Mode
## a0 77 -none- numeric
## beta 154 dgCMatrix S4
## df 77 -none- numeric
## dim 2 -none- numeric
## lambda 77 -none- numeric
## dev.ratio 77 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) startprice.dcm2.is9 startprice.dgt1.is9
## 1.703600 -1.308097 0.904405
## [1] "max lambda < lambdaOpt:"
## (Intercept) startprice.dcm2.is9 startprice.dgt1.is9
## 1.7039153 -1.3099341 0.9060087
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats
## 1 Max.cor.Y.rcv.1X1.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.85 0.01
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.5231009 0.6473136 0.5215576 0.1530671 1.069369
## max.Adj.R.sq.OOB
## 1 0.1501814# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=rcv_n_folds, trainControl.repeats=rcv_n_repeats,
train.method="glmnet")),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
}## [1] "fitting model: Max.cor.Y.rcv.3X1.glmnet"
## [1] " indep_vars: startprice.dcm2.is9,startprice.dgt1.is9"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.00243 on full training set
## Length Class Mode
## a0 59 -none- numeric
## beta 118 dgCMatrix S4
## df 59 -none- numeric
## dim 2 -none- numeric
## lambda 59 -none- numeric
## dev.ratio 59 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) startprice.dcm2.is9 startprice.dgt1.is9
## 1.7062498 -1.3211869 0.9140367
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "startprice.dcm2.is9" "startprice.dgt1.is9"
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats
## 1 Max.cor.Y.rcv.3X1.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.235 0.005
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.523203 0.6483723 0.52166 0.1519868 1.070051
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1490975 0.5230352 0.01939618 0.01019279
## [1] "fitting model: Max.cor.Y.rcv.3X3.glmnet"
## [1] " indep_vars: startprice.dcm2.is9,startprice.dgt1.is9"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.00243 on full training set
## Length Class Mode
## a0 62 -none- numeric
## beta 124 dgCMatrix S4
## df 62 -none- numeric
## dim 2 -none- numeric
## lambda 62 -none- numeric
## dev.ratio 62 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) startprice.dcm2.is9 startprice.dgt1.is9
## 1.7061726 -1.3214199 0.9147639
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "startprice.dcm2.is9" "startprice.dgt1.is9"
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats
## 1 Max.cor.Y.rcv.3X3.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 4.184 0.007
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.5232057 0.6488737 0.5216627 0.1520071 1.070038
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1491179 0.5245475 0.04964437 0.05637477
## [1] "fitting model: Max.cor.Y.rcv.3X5.glmnet"
## [1] " indep_vars: startprice.dcm2.is9,startprice.dgt1.is9"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.00243 on full training set
## Length Class Mode
## a0 62 -none- numeric
## beta 124 dgCMatrix S4
## df 62 -none- numeric
## dim 2 -none- numeric
## lambda 62 -none- numeric
## dev.ratio 62 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) startprice.dcm2.is9 startprice.dgt1.is9
## 1.7061726 -1.3214199 0.9147639
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "startprice.dcm2.is9" "startprice.dgt1.is9"
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats
## 1 Max.cor.Y.rcv.3X5.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.167 0.005
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.5232057 0.6491051 0.5216627 0.1520071 1.070038
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1491179 0.5249564 0.05053156 0.05992791
## [1] "fitting model: Max.cor.Y.rcv.5X1.glmnet"
## [1] " indep_vars: startprice.dcm2.is9,startprice.dgt1.is9"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.00243 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 77 -none- numeric
## beta 154 dgCMatrix S4
## df 77 -none- numeric
## dim 2 -none- numeric
## lambda 77 -none- numeric
## dev.ratio 77 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) startprice.dcm2.is9 startprice.dgt1.is9
## 1.7057638 -1.3207083 0.9154232
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "startprice.dcm2.is9" "startprice.dgt1.is9"
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats
## 1 Max.cor.Y.rcv.5X1.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.48 0.005
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.5232047 0.6486277 0.5216617 0.1521632 1.069939
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1492745 0.5273959 0.05853097 0.07367248
## [1] "fitting model: Max.cor.Y.rcv.5X3.glmnet"
## [1] " indep_vars: startprice.dcm2.is9,startprice.dgt1.is9"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.00243 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 77 -none- numeric
## beta 154 dgCMatrix S4
## df 77 -none- numeric
## dim 2 -none- numeric
## lambda 77 -none- numeric
## dev.ratio 77 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) startprice.dcm2.is9 startprice.dgt1.is9
## 1.7057638 -1.3207083 0.9154232
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "startprice.dcm2.is9" "startprice.dgt1.is9"
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats
## 1 Max.cor.Y.rcv.5X3.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.056 0.004
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.5232047 0.647974 0.5216617 0.1521632 1.069939
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1492745 0.5269525 0.05541664 0.06285653
## [1] "fitting model: Max.cor.Y.rcv.5X5.glmnet"
## [1] " indep_vars: startprice.dcm2.is9,startprice.dgt1.is9"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.00243 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
## list(id.prefix = paste0("Max.cor.Y.rcv.", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 77 -none- numeric
## beta 154 dgCMatrix S4
## df 77 -none- numeric
## dim 2 -none- numeric
## lambda 77 -none- numeric
## dev.ratio 77 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 2 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept) startprice.dcm2.is9 startprice.dgt1.is9
## 1.7057638 -1.3207083 0.9154232
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" "startprice.dcm2.is9" "startprice.dgt1.is9"
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats
## 1 Max.cor.Y.rcv.5X5.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.377 0.006
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.5232047 0.6474622 0.5216617 0.1521632 1.069939
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1492745 0.5265158 0.05386228 0.05883978# Add parallel coordinates graph of glb_models_df[, glb_model_evl_criteria] to evaluate cv parameters
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Max.cor.Y.rcv.1X1.cp.0", type=glb_model_type, trainControl.method="none",
train.method="rpart",
tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.rcv.1X1.cp.0.rpart"
## [1] " indep_vars: startprice.dcm2.is9,startprice.dgt1.is9"## Loading required package: rpart## Fitting cp = 0 on full training set## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 621
##
## CP nsplit rel error
## 1 0.3616214855 0 1.0000000
## 2 0.2998481926 1 0.6383785
## 3 0.0007068855 2 0.3385303
## 4 0.0000000000 3 0.3378234
##
## Variable importance
## startprice.dcm2.is9 startprice.dgt1.is9
## 53 47
##
## Node number 1: 621 observations, complexity param=0.3616215
## mean=1.425768, MSE=0.8786238
## left son=2 (228 obs) right son=3 (393 obs)
## Primary splits:
## startprice.dcm2.is9 < 0.5 to the right, improve=0.36162150, (0 missing)
## startprice.dgt1.is9 < 0.5 to the left, improve=0.07482525, (0 missing)
## Surrogate splits:
## startprice.dgt1.is9 < 0.5 to the right, agree=0.654, adj=0.057, (0 split)
##
## Node number 2: 228 observations, complexity param=0.2998482
## mean=0.6857237, MSE=0.8887653
## left son=4 (152 obs) right son=5 (76 obs)
## Primary splits:
## startprice.dgt1.is9 < 0.5 to the left, improve=0.8073727, (0 missing)
##
## Node number 3: 393 observations, complexity param=0.0007068855
## mean=1.855106, MSE=0.3706795
## left son=6 (330 obs) right son=7 (63 obs)
## Primary splits:
## startprice.dgt1.is9 < 0.5 to the left, improve=0.002647601, (0 missing)
##
## Node number 4: 152 observations
## mean=0.08673919, MSE=0.1551424
##
## Node number 5: 76 observations
## mean=1.883693, MSE=0.2033167
##
## Node number 6: 330 observations
## mean=1.841418, MSE=0.4115119
##
## Node number 7: 63 observations
## mean=1.926805, MSE=0.1506732
##
## n= 621
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 621 545.62540 1.42576800
## 2) startprice.dcm2.is9>=0.5 228 202.63850 0.68572370
## 4) startprice.dgt1.is9< 0.5 152 23.58164 0.08673919 *
## 5) startprice.dgt1.is9>=0.5 76 15.45207 1.88369300 *
## 3) startprice.dcm2.is9< 0.5 393 145.67700 1.85510600
## 6) startprice.dgt1.is9< 0.5 330 135.79890 1.84141800 *
## 7) startprice.dgt1.is9>=0.5 63 9.49241 1.92680500 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats
## 1 Max.cor.Y.rcv.1X1.cp.0.rpart startprice.dcm2.is9,startprice.dgt1.is9
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 0 0.697 0.01
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.6621766 0.5448116 NA 0.2295076 1.01997
## max.Adj.R.sq.OOB
## 1 NA# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Max.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
train.method="rpart")),
indep_vars=max_cor_y_x_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Max.cor.Y.rpart"
## [1] " indep_vars: startprice.dcm2.is9,startprice.dgt1.is9"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.181 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 621
##
## CP nsplit rel error
## 1 0.3616215 0 1.0000000
## 2 0.2998482 1 0.6383785
## 3 0.1808107 2 0.3385303
##
## Variable importance
## startprice.dcm2.is9 startprice.dgt1.is9
## 53 47
##
## Node number 1: 621 observations, complexity param=0.3616215
## mean=1.425768, MSE=0.8786238
## left son=2 (228 obs) right son=3 (393 obs)
## Primary splits:
## startprice.dcm2.is9 < 0.5 to the right, improve=0.36162150, (0 missing)
## startprice.dgt1.is9 < 0.5 to the left, improve=0.07482525, (0 missing)
## Surrogate splits:
## startprice.dgt1.is9 < 0.5 to the right, agree=0.654, adj=0.057, (0 split)
##
## Node number 2: 228 observations, complexity param=0.2998482
## mean=0.6857237, MSE=0.8887653
## left son=4 (152 obs) right son=5 (76 obs)
## Primary splits:
## startprice.dgt1.is9 < 0.5 to the left, improve=0.8073727, (0 missing)
##
## Node number 3: 393 observations
## mean=1.855106, MSE=0.3706795
##
## Node number 4: 152 observations
## mean=0.08673919, MSE=0.1551424
##
## Node number 5: 76 observations
## mean=1.883693, MSE=0.2033167
##
## n= 621
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 621 545.62540 1.42576800
## 2) startprice.dcm2.is9>=0.5 228 202.63850 0.68572370
## 4) startprice.dgt1.is9< 0.5 152 23.58164 0.08673919 *
## 5) startprice.dgt1.is9>=0.5 76 15.45207 1.88369300 *
## 3) startprice.dcm2.is9< 0.5 393 145.67700 1.85510600 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id feats max.nTuningRuns
## 1 Max.cor.Y.rpart startprice.dcm2.is9,startprice.dgt1.is9 5
## min.elapsedtime.everything min.elapsedtime.final max.R.sq.fit
## 1 1.177 0.009 0.6614697
## min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB max.Adj.R.sq.OOB
## 1 0.5425538 NA 0.2237461 1.023776 NA
## max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.6647272 0.08229901 0.09004506if (!is.null(glb_date_vars) &&
(sum(grepl(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df))) > 0)) {
# ret_lst <- myfit_mdl(mdl_id="Max.cor.Y.TmSrs.poly1",
# model_method=ifelse(glb_is_regression, "lm",
# ifelse(glb_is_binomial, "glm", "rpart")),
# model_type=glb_model_type,
# indep_vars_vctr=c(max_cor_y_x_vars, paste0(glb_date_vars, ".day.minutes")),
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=NULL)
#
ret_lst <- myfit_mdl(mdl_id="Max.cor.Y.TmSrs.poly",
model_method=ifelse(glb_is_regression, "lm",
ifelse(glb_is_binomial, "glm", "rpart")),
model_type=glb_model_type,
indep_vars_vctr=c(max_cor_y_x_vars,
grep(paste(glb_date_vars, "\\.day\\.minutes\\.poly\\.", sep=""),
names(glb_allobs_df), value=TRUE)),
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
n_cv_folds=glb_rcv_n_folds, tune_models_df=NULL)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
train.method="glmnet")),
indep_vars=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
} ## [1] "fitting model: Interact.High.cor.Y.glmnet"
## [1] " indep_vars: startprice.dcm2.is9,startprice.dgt1.is9,startprice.dcm2.is9:cellular.fctr,startprice.dcm2.is9:D.chrs.pnct13.n.log,startprice.dcm2.is9:D.chrs.uppr.n.log,startprice.dcm2.is9:D.weight.post.stem.sum,startprice.dcm2.is9:D.terms.post.stem.n.log,startprice.dcm2.is9:D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9:startprice.dcm2.is9"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.0113 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Interact.High.cor.Y", : model's bestTune found at an
## extreme of tuneGrid for parameter: alpha
## Length Class Mode
## a0 84 -none- numeric
## beta 756 dgCMatrix S4
## df 84 -none- numeric
## dim 2 -none- numeric
## lambda 84 -none- numeric
## dev.ratio 84 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 9 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 1.70405374
## startprice.dcm2.is9
## -1.27196199
## startprice.dgt1.is9
## 0.88794458
## startprice.dcm2.is9:D.chrs.pnct13.n.log
## -0.05245117
## startprice.dcm2.is9:D.weight.post.stem.sum
## -0.00429766
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 1.70434615
## startprice.dcm2.is9
## -1.27384502
## startprice.dgt1.is9
## 0.89094438
## startprice.dcm2.is9:D.chrs.pnct13.n.log
## -0.05434781
## startprice.dcm2.is9:D.weight.post.stem.sum
## -0.00435936
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 Interact.High.cor.Y.glmnet
## feats
## 1 startprice.dcm2.is9,startprice.dgt1.is9,startprice.dcm2.is9:cellular.fctr,startprice.dcm2.is9:D.chrs.pnct13.n.log,startprice.dcm2.is9:D.chrs.uppr.n.log,startprice.dcm2.is9:D.weight.post.stem.sum,startprice.dcm2.is9:D.terms.post.stem.n.log,startprice.dcm2.is9:D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9:startprice.dcm2.is9
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.755 0.006
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.5236562 0.6508063 0.5166397 0.1555183 1.06782
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1424143 0.5217293 0.04930768 0.05705445# Low.cor.X
# if (glb_is_classification && glb_is_binomial)
# indep_vars_vctr <- subset(glb_feats_df, is.na(cor.high.X) &
# is.ConditionalX.y &
# (exclude.as.feat != 1))[, "id"] else
indep_vars <- subset(glb_feats_df, is.na(cor.high.X) & !nzv &
(exclude.as.feat != 1))[, "id"]
indep_vars <- myadjust_interaction_feats(indep_vars)
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Low.cor.X",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
train.method="glmnet")),
indep_vars=indep_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)## [1] "fitting model: Low.cor.X.glmnet"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dgt2.is9,D.ratio.wrds.stop.n.wrds.n,prdl.descr.my.fctr,D.weight.sum.stem.stop.Ratio,storage.fctr,.rnorm,color.fctr,condition.fctr,cellular.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,D.ratio.weight.sum.wrds.n,startprice.dcm2.is9,prdl.descr.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.0523 on full training set
## Length Class Mode
## a0 82 -none- numeric
## beta 10086 dgCMatrix S4
## df 82 -none- numeric
## dim 2 -none- numeric
## lambda 82 -none- numeric
## dev.ratio 82 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 123 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 1.734219460
## D.ratio.weight.sum.wrds.n
## -0.144552089
## color.fctrUnknown
## -0.033010135
## condition.fctrFor parts or not working
## -0.205071314
## condition.fctrNew
## 0.095224035
## condition.fctrSeller refurbished
## -0.099871181
## prdl.descr.my.fctriPad1#0
## -0.009694594
## prdl.descr.my.fctriPad2#0
## -0.004576779
## prdl.descr.my.fctriPadAir#0
## 0.031520011
## prdl.descr.my.fctriPadAir2#0
## 0.194774521
## prdl.descr.my.fctriPadAir2#1
## 0.011546730
## startprice.dcm2.is9
## -1.146706285
## startprice.dgt1.is9
## 0.672351225
## startprice.dgt2.is9
## 0.259327748
## prdl.descr.my.fctriPad1#1:.clusterid.fctr3
## -0.013761375
## prdl.descr.my.fctriPad3#1:.clusterid.fctr3
## -0.086545615
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr3
## -0.186868028
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3
## 0.173723508
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr5
## 0.111739511
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 1.74280241
## D.ratio.weight.sum.wrds.n
## -0.15074478
## color.fctrUnknown
## -0.04057013
## condition.fctrFor parts or not working
## -0.21479420
## condition.fctrNew
## 0.09736448
## condition.fctrSeller refurbished
## -0.11237398
## prdl.descr.my.fctriPad1#0
## -0.02618263
## prdl.descr.my.fctriPad2#0
## -0.02012065
## prdl.descr.my.fctriPadAir#0
## 0.04222659
## prdl.descr.my.fctriPadAir2#0
## 0.20361159
## prdl.descr.my.fctriPadAir2#1
## 0.03746805
## startprice.dcm2.is9
## -1.15526366
## startprice.dgt1.is9
## 0.67988072
## startprice.dgt2.is9
## 0.26452452
## prdl.descr.my.fctriPad3#1:.clusterid.fctr2
## 0.01085444
## prdl.descr.my.fctriPad1#1:.clusterid.fctr3
## -0.03275108
## prdl.descr.my.fctriPad3#1:.clusterid.fctr3
## -0.13410141
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr3
## -0.24048049
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3
## 0.23604792
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr5
## 0.16812022
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 Low.cor.X.glmnet
## feats
## 1 startprice.dgt1.is9,startprice.dgt2.is9,D.ratio.wrds.stop.n.wrds.n,prdl.descr.my.fctr,D.weight.sum.stem.stop.Ratio,storage.fctr,.rnorm,color.fctr,condition.fctr,cellular.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,D.ratio.weight.sum.wrds.n,startprice.dcm2.is9,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.846 0.013
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.5761879 0.6377835 0.4713008 0.2003791 1.039071
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 -0.01067969 0.5447478 0.03682266 0.04391066rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 10 fit.models 7 0 0 117.87 156.489 38.619
## 11 fit.models 7 1 1 156.49 NA NAfit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor="setup")## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 159.957 NA NA#stop(here"); glb_to_sav(); glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glb_mdl_family_lst)) {
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", mdl_id_pfx), major.inc=TRUE, label.minor="setup")
indep_vars <- NULL;
if (grepl("\\.Interact", mdl_id_pfx)) {
# !_sp
# interact_vars_vctr <- c(
# "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio",
# "D.npnct15.log", "D.npnct03.log", "D.wrds.n.log", "D.chrs.n.log")
# indep_vars <- union(setdiff(indep_vars, interact_vars_vctr),
# paste(glb_category_var, interact_vars_vctr,
# sep=ifelse(grepl("\\.fctr", glb_category_var), "*", ".fctr*")))
# indep_vars <- union(setdiff(indep_vars,
# c("startprice.log.diff", "startprice.unit9", "biddable", "cellular.fctr", "carrier.fctr")),
# c("startprice.log.diff*biddable", "startprice.unit9*biddable", "cellular.fctr*carrier.fctr"))
###
# _sp only
if (is.null(topindep_var) && is.null(interact_vars)) {
# select best glmnet model upto now
dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(), glb_models_df)
dsp_models_df <- subset(dsp_models_df, grepl(".glmnet", id, fixed=TRUE))
bst_mdl_id <- dsp_models_df$id[1]
mdl_id_pfx <-
paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
collapse=".")
# select most importance feature
if (is.null(bst_featsimp_df <-
myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
warning("Base model for RFE.Interact: ", bst_mdl_id,
" has no important features")
next
}
topindep_ix <- 1
while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
if (grepl(".fctr", topindep_var, fixed=TRUE))
topindep_var <- paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
if (topindep_var %in% names(glb_interaction_only_feats_lst)) {
topindep_var <- NULL; topindep_ix <- topindep_ix + 1
} else break
}
# select features with importance > max(10, importance of .rnorm) & is not highest
# combine factor dummy features to just the factor feature
if (length(pos_rnorm <-
grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
imp_rnorm <- NA
importance_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
interact_vars <-
tail(row.names(subset(bst_featsimp_df, importance > importance_cutoff)), -1)
interact_vars <- myadjust_interaction_feats(myextract_actual_feats(interact_vars))
interact_vars <- interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
### bid0_sp only
# interact_vars <- c(
# "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
# "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
# "D.chrs.n.log", "color.fctr"
# # , "condition.fctr", "prdl.my.descr.fctr"
# )
# interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
###
indep_vars <- myextract_actual_feats(row.names(bst_featsimp_df))
indep_vars <- setdiff(indep_vars, topindep_var)
indep_vars <- setdiff(indep_vars, myextract_actual_feats(interact_vars))
indep_vars <- c(indep_vars,
paste(topindep_var, setdiff(interact_vars, topindep_var), sep="*"))
}
}
if (is.null(indep_vars))
indep_vars <- glb_mdl_feats_lst[[mdl_id_pfx]]
if (is.null(indep_vars) && grepl("RFE\\.", mdl_id_pfx))
indep_vars <- myextract_actual_feats(predictors(rfe_fit_results))
if (is.null(indep_vars))
indep_vars <- subset(glb_feats_df, !nzv & (exclude.as.feat != 1))[, "id"]
indep_vars <- myadjust_interaction_feats(indep_vars)
if (grepl("\\.Interact", mdl_id_pfx)) {
# if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
if (is.null(glb_mdl_family_lst[[mdl_id_pfx]])) {
if (!is.null(glb_mdl_family_lst[["Best.Interact"]]))
glb_mdl_family_lst[[mdl_id_pfx]] <- glb_mdl_family_lst[["Best.Interact"]]
}
}
if (is.null(glb_mdl_family_lst[[mdl_id_pfx]]))
mdl_methods <- glb_mdl_methods else
mdl_methods <- glb_mdl_family_lst[[mdl_id_pfx]]
for (method in mdl_methods) {
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indep_vars <- setdiff(indep_vars, c(".rnorm"))
#mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
}
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
label.minor = method)
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type=glb_model_type, tune.df=glb_tune_models_df,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
train.method=method)),
indep_vars=indep_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
}
} ## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 159.957 159.966
## 2 fit.models_1_RFE.X 2 0 setup 159.967 NA
## elapsed
## 1 0.009
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_RFE.X 2 0 setup 159.967 159.973
## 3 fit.models_1_RFE.X 2 1 glm 159.973 NA
## elapsed
## 2 0.006
## 3 NA
## [1] "fitting model: RFE.X.glm"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr"
## Aggregating results
## Fitting final model on full training set
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.50440 -0.34548 0.05931 0.35742 1.85208
##
## Coefficients: (13 not defined because of singularities)
## Estimate Std. Error t value
## (Intercept) 1.89650 0.14311 13.252
## D.ratio.weight.sum.wrds.n -0.31863 0.05168 -6.165
## cellular.fctr1 -0.02625 0.08467 -0.310
## cellular.fctrUnknown -0.11432 0.11975 -0.955
## color.fctrGold -0.46109 0.19116 -2.412
## `color.fctrSpace Gray` -0.06687 0.09638 -0.694
## color.fctrUnknown -0.17368 0.06645 -2.614
## color.fctrWhite -0.03817 0.07322 -0.521
## `condition.fctrFor parts or not working` -0.26686 0.07382 -3.615
## `condition.fctrManufacturer refurbished` -0.41746 0.22565 -1.850
## condition.fctrNew 0.13782 0.10559 1.305
## `condition.fctrNew other (see details)` 0.11207 0.14383 0.779
## `condition.fctrSeller refurbished` -0.15519 0.13505 -1.149
## `prdl.descr.my.fctrUnknown#1` 0.46188 0.16879 2.736
## `prdl.descr.my.fctriPad1#0` -0.20332 0.15601 -1.303
## `prdl.descr.my.fctriPad1#1` -0.07900 0.16318 -0.484
## `prdl.descr.my.fctriPad2#0` -0.11884 0.15330 -0.775
## `prdl.descr.my.fctriPad2#1` 0.18048 0.16638 1.085
## `prdl.descr.my.fctriPad3#0` -0.18244 0.16158 -1.129
## `prdl.descr.my.fctriPad3#1` 0.10143 0.19455 0.521
## `prdl.descr.my.fctriPad4#0` 0.11124 0.17558 0.634
## `prdl.descr.my.fctriPad4#1` 0.04603 0.20342 0.226
## `prdl.descr.my.fctriPadAir#0` 0.19553 0.16586 1.179
## `prdl.descr.my.fctriPadAir#1` 0.19885 0.17906 1.111
## `prdl.descr.my.fctriPadAir2#0` 0.36823 0.17623 2.089
## `prdl.descr.my.fctriPadAir2#1` 0.44263 0.21441 2.064
## `prdl.descr.my.fctriPadmini#0` 0.01673 0.15196 0.110
## `prdl.descr.my.fctriPadmini#1` 0.03889 0.16678 0.233
## `prdl.descr.my.fctriPadmini2#0` 0.13596 0.18321 0.742
## `prdl.descr.my.fctriPadmini2#1` 0.43873 0.23535 1.864
## `prdl.descr.my.fctriPadmini3#0` -0.05738 0.20543 -0.279
## `prdl.descr.my.fctriPadmini3#1` 0.36130 0.37440 0.965
## startprice.dcm1.is9 -0.81063 0.13000 -6.235
## startprice.dcm2.is9 -0.55184 0.12605 -4.378
## startprice.dgt1.is9 0.92816 0.07410 12.526
## startprice.dgt2.is9 0.13387 0.09520 1.406
## `cellular.fctr0:carrier.fctrNone` NA NA NA
## `cellular.fctr1:carrier.fctrNone` NA NA NA
## `cellular.fctrUnknown:carrier.fctrNone` NA NA NA
## `cellular.fctr0:carrier.fctrOther` NA NA NA
## `cellular.fctr1:carrier.fctrOther` 1.68281 0.47087 3.574
## `cellular.fctrUnknown:carrier.fctrOther` NA NA NA
## `cellular.fctr0:carrier.fctrSprint` NA NA NA
## `cellular.fctr1:carrier.fctrSprint` -0.65517 0.24049 -2.724
## `cellular.fctrUnknown:carrier.fctrSprint` NA NA NA
## `cellular.fctr0:carrier.fctrT-Mobile` NA NA NA
## `cellular.fctr1:carrier.fctrT-Mobile` -0.20312 0.26479 -0.767
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA NA NA
## `cellular.fctr0:carrier.fctrUnknown` NA NA NA
## `cellular.fctr1:carrier.fctrUnknown` 0.14808 0.12539 1.181
## `cellular.fctrUnknown:carrier.fctrUnknown` NA NA NA
## `cellular.fctr0:carrier.fctrVerizon` NA NA NA
## `cellular.fctr1:carrier.fctrVerizon` -0.02760 0.12078 -0.229
## `cellular.fctrUnknown:carrier.fctrVerizon` NA NA NA
## Pr(>|t|)
## (Intercept) < 2e-16 ***
## D.ratio.weight.sum.wrds.n 1.32e-09 ***
## cellular.fctr1 0.756610
## cellular.fctrUnknown 0.340153
## color.fctrGold 0.016173 *
## `color.fctrSpace Gray` 0.488043
## color.fctrUnknown 0.009185 **
## color.fctrWhite 0.602328
## `condition.fctrFor parts or not working` 0.000326 ***
## `condition.fctrManufacturer refurbished` 0.064820 .
## condition.fctrNew 0.192329
## `condition.fctrNew other (see details)` 0.436167
## `condition.fctrSeller refurbished` 0.250990
## `prdl.descr.my.fctrUnknown#1` 0.006402 **
## `prdl.descr.my.fctriPad1#0` 0.193015
## `prdl.descr.my.fctriPad1#1` 0.628507
## `prdl.descr.my.fctriPad2#0` 0.438547
## `prdl.descr.my.fctriPad2#1` 0.278488
## `prdl.descr.my.fctriPad3#0` 0.259317
## `prdl.descr.my.fctriPad3#1` 0.602301
## `prdl.descr.my.fctriPad4#0` 0.526617
## `prdl.descr.my.fctriPad4#1` 0.821063
## `prdl.descr.my.fctriPadAir#0` 0.238921
## `prdl.descr.my.fctriPadAir#1` 0.267235
## `prdl.descr.my.fctriPadAir2#0` 0.037102 *
## `prdl.descr.my.fctriPadAir2#1` 0.039420 *
## `prdl.descr.my.fctriPadmini#0` 0.912359
## `prdl.descr.my.fctriPadmini#1` 0.815686
## `prdl.descr.my.fctriPadmini2#0` 0.458324
## `prdl.descr.my.fctriPadmini2#1` 0.062806 .
## `prdl.descr.my.fctriPadmini3#0` 0.780087
## `prdl.descr.my.fctriPadmini3#1` 0.334945
## startprice.dcm1.is9 8.68e-10 ***
## startprice.dcm2.is9 1.42e-05 ***
## startprice.dgt1.is9 < 2e-16 ***
## startprice.dgt2.is9 0.160218
## `cellular.fctr0:carrier.fctrNone` NA
## `cellular.fctr1:carrier.fctrNone` NA
## `cellular.fctrUnknown:carrier.fctrNone` NA
## `cellular.fctr0:carrier.fctrOther` NA
## `cellular.fctr1:carrier.fctrOther` 0.000381 ***
## `cellular.fctrUnknown:carrier.fctrOther` NA
## `cellular.fctr0:carrier.fctrSprint` NA
## `cellular.fctr1:carrier.fctrSprint` 0.006637 **
## `cellular.fctrUnknown:carrier.fctrSprint` NA
## `cellular.fctr0:carrier.fctrT-Mobile` NA
## `cellular.fctr1:carrier.fctrT-Mobile` 0.443353
## `cellular.fctrUnknown:carrier.fctrT-Mobile` NA
## `cellular.fctr0:carrier.fctrUnknown` NA
## `cellular.fctr1:carrier.fctrUnknown` 0.238109
## `cellular.fctrUnknown:carrier.fctrUnknown` NA
## `cellular.fctr0:carrier.fctrVerizon` NA
## `cellular.fctr1:carrier.fctrVerizon` 0.819317
## `cellular.fctrUnknown:carrier.fctrVerizon` NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 0.3331841)
##
## Null deviance: 545.63 on 620 degrees of freedom
## Residual deviance: 193.25 on 580 degrees of freedom
## AIC: 1121.4
##
## Number of Fisher Scoring iterations: 2
##
## [1] " calling mypredict_mdl for fit:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## [1] " calling mypredict_mdl for OOB:"## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## id
## 1 RFE.X.glm
## feats
## 1 startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.198 0.028
## max.R.sq.fit min.RMSE.fit min.aic.fit max.Adj.R.sq.fit max.R.sq.OOB
## 1 0.6458252 0.6340075 1121.389 0.6213994 0.1657697
## min.RMSE.OOB max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit
## 1 1.061319 0.1049878 0.555028 0.04132701
## max.RsquaredSD.fit
## 1 0.04178663
## label step_major step_minor label_minor bgn end
## 3 fit.models_1_RFE.X 2 1 glm 159.973 162.962
## 4 fit.models_1_RFE.X 2 2 glmnet 162.962 NA
## elapsed
## 3 2.989
## 4 NA
## [1] "fitting model: RFE.X.glmnet"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.325, lambda = 0.0523 on full training set
## Length Class Mode
## a0 79 -none- numeric
## beta 4187 dgCMatrix S4
## df 79 -none- numeric
## dim 2 -none- numeric
## lambda 79 -none- numeric
## dev.ratio 79 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 53 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 1.833423521
## D.ratio.weight.sum.wrds.n
## -0.212598627
## cellular.fctrUnknown
## -0.023656683
## color.fctrGold
## -0.106947765
## color.fctrUnknown
## -0.088359348
## condition.fctrFor parts or not working
## -0.227464644
## condition.fctrManufacturer refurbished
## -0.199764490
## condition.fctrNew
## 0.085623187
## condition.fctrNew other (see details)
## 0.065254605
## condition.fctrSeller refurbished
## -0.115610842
## prdl.descr.my.fctrUnknown#1
## 0.128679782
## prdl.descr.my.fctriPad1#0
## -0.122735907
## prdl.descr.my.fctriPad1#1
## -0.085180213
## prdl.descr.my.fctriPad2#0
## -0.084867345
## prdl.descr.my.fctriPad3#0
## -0.098962546
## prdl.descr.my.fctriPad4#0
## 0.012760118
## prdl.descr.my.fctriPadAir#0
## 0.080494627
## prdl.descr.my.fctriPadAir2#0
## 0.250974301
## prdl.descr.my.fctriPadAir2#1
## 0.155643707
## prdl.descr.my.fctriPadmini2#0
## 0.006640484
## prdl.descr.my.fctriPadmini2#1
## 0.169824833
## startprice.dcm1.is9
## -0.674586588
## startprice.dcm2.is9
## -0.584741390
## startprice.dgt1.is9
## 0.826184134
## startprice.dgt2.is9
## 0.170175235
## cellular.fctr1:carrier.fctrOther
## 0.898216573
## cellular.fctr1:carrier.fctrSprint
## -0.389404484
## cellular.fctr1:carrier.fctrUnknown
## 0.024647713
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.008299165
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 1.83859147
## D.ratio.weight.sum.wrds.n
## -0.22011328
## cellular.fctrUnknown
## -0.02807367
## color.fctrGold
## -0.13163113
## color.fctrUnknown
## -0.09315509
## condition.fctrFor parts or not working
## -0.23102046
## condition.fctrManufacturer refurbished
## -0.21805349
## condition.fctrNew
## 0.08783574
## condition.fctrNew other (see details)
## 0.07062776
## condition.fctrSeller refurbished
## -0.11850642
## prdl.descr.my.fctrUnknown#1
## 0.15370047
## prdl.descr.my.fctriPad1#0
## -0.13129978
## prdl.descr.my.fctriPad1#1
## -0.08815197
## prdl.descr.my.fctriPad2#0
## -0.09072019
## prdl.descr.my.fctriPad2#1
## 0.01033477
## prdl.descr.my.fctriPad3#0
## -0.10903725
## prdl.descr.my.fctriPad4#0
## 0.01841698
## prdl.descr.my.fctriPadAir#0
## 0.08600075
## prdl.descr.my.fctriPadAir#1
## 0.01030761
## prdl.descr.my.fctriPadAir2#0
## 0.25793725
## prdl.descr.my.fctriPadAir2#1
## 0.17331090
## prdl.descr.my.fctriPadmini2#0
## 0.01318240
## prdl.descr.my.fctriPadmini2#1
## 0.18265188
## startprice.dcm1.is9
## -0.68373383
## startprice.dcm2.is9
## -0.58352549
## startprice.dgt1.is9
## 0.83425836
## startprice.dgt2.is9
## 0.16733727
## cellular.fctr1:carrier.fctrOther
## 0.95978448
## cellular.fctr1:carrier.fctrSprint
## -0.41304994
## cellular.fctr1:carrier.fctrUnknown
## 0.03334763
## cellular.fctrUnknown:carrier.fctrUnknown
## -0.01244343
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.glmnet
## feats
## 1 startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.715 0.008
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.6287589 0.6164891 0.5940574 0.1976359 1.040852
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1182977 0.5730742 0.04123952 0.04770118
## label step_major step_minor label_minor bgn end
## 4 fit.models_1_RFE.X 2 2 glmnet 162.962 166.663
## 5 fit.models_1_RFE.X 2 3 rpart 166.663 NA
## elapsed
## 4 3.701
## 5 NA
## [1] "fitting model: RFE.X.rpart"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.003 on full training set
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 621
##
## CP nsplit rel error
## 1 0.361621486 0 1.0000000
## 2 0.299848193 1 0.6383785
## 3 0.027590245 2 0.3385303
## 4 0.016191995 3 0.3109401
## 5 0.008081388 4 0.2947481
## 6 0.005222924 5 0.2866667
## 7 0.004447308 7 0.2762208
## 8 0.003385734 8 0.2717735
## 9 0.003000000 11 0.2616163
##
## Variable importance
## startprice.dcm2.is9
## 29
## startprice.dgt1.is9
## 26
## startprice.dcm1.is9
## 26
## startprice.dgt2.is9
## 9
## D.ratio.weight.sum.wrds.n
## 3
## condition.fctrFor parts or not working
## 1
## condition.fctrSeller refurbished
## 1
## prdl.descr.my.fctrUnknown#1
## 1
## prdl.descr.my.fctriPadAir2#0
## 1
## cellular.fctr1:carrier.fctrOther
## 1
##
## Node number 1: 621 observations, complexity param=0.3616215
## mean=1.425768, MSE=0.8786238
## left son=2 (228 obs) right son=3 (393 obs)
## Primary splits:
## startprice.dcm2.is9 < 0.5 to the right, improve=0.36162150, (0 missing)
## startprice.dcm1.is9 < 0.5 to the right, improve=0.33039880, (0 missing)
## startprice.dgt1.is9 < 0.5 to the left, improve=0.07482525, (0 missing)
## startprice.dgt2.is9 < 0.5 to the left, improve=0.06377029, (0 missing)
## D.ratio.weight.sum.wrds.n < 0.3588254 to the right, improve=0.04147296, (0 missing)
## Surrogate splits:
## startprice.dcm1.is9 < 0.5 to the right, agree=0.958, adj=0.886, (0 split)
## startprice.dgt1.is9 < 0.5 to the right, agree=0.654, adj=0.057, (0 split)
## condition.fctrSeller refurbished < 0.5 to the right, agree=0.644, adj=0.031, (0 split)
## prdl.descr.my.fctrUnknown#1 < 0.5 to the right, agree=0.643, adj=0.026, (0 split)
## D.ratio.weight.sum.wrds.n < 0.5816328 to the right, agree=0.639, adj=0.018, (0 split)
##
## Node number 2: 228 observations, complexity param=0.2998482
## mean=0.6857237, MSE=0.8887653
## left son=4 (152 obs) right son=5 (76 obs)
## Primary splits:
## startprice.dgt1.is9 < 0.5 to the left, improve=0.80737270, (0 missing)
## startprice.dgt2.is9 < 0.5 to the left, improve=0.32636950, (0 missing)
## D.ratio.weight.sum.wrds.n < 0.3579744 to the right, improve=0.03583546, (0 missing)
## color.fctrUnknown < 0.5 to the right, improve=0.03564657, (0 missing)
## condition.fctrSeller refurbished < 0.5 to the right, improve=0.02201806, (0 missing)
## Surrogate splits:
## startprice.dgt2.is9 < 0.5 to the left, agree=0.781, adj=0.342, (0 split)
## cellular.fctr1:carrier.fctrOther < 0.5 to the left, agree=0.675, adj=0.026, (0 split)
## color.fctrWhite < 0.5 to the left, agree=0.671, adj=0.013, (0 split)
##
## Node number 3: 393 observations, complexity param=0.02759025
## mean=1.855106, MSE=0.3706795
## left son=6 (9 obs) right son=7 (384 obs)
## Primary splits:
## D.ratio.weight.sum.wrds.n < 1.958818 to the right, improve=0.10333780, (0 missing)
## condition.fctrFor parts or not working < 0.5 to the right, improve=0.06417654, (0 missing)
## prdl.descr.my.fctriPadAir2#0 < 0.5 to the left, improve=0.04472189, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.03280389, (0 missing)
## color.fctrSpace Gray < 0.5 to the left, improve=0.02663307, (0 missing)
##
## Node number 4: 152 observations
## mean=0.08673919, MSE=0.1551424
##
## Node number 5: 76 observations, complexity param=0.005222924
## mean=1.883693, MSE=0.2033167
## left son=10 (49 obs) right son=11 (27 obs)
## Primary splits:
## startprice.dgt2.is9 < 0.5 to the left, improve=0.16578700, (0 missing)
## D.ratio.weight.sum.wrds.n < 0.3317462 to the right, improve=0.06408874, (0 missing)
## prdl.descr.my.fctriPad2#1 < 0.5 to the right, improve=0.05467352, (0 missing)
## condition.fctrFor parts or not working < 0.5 to the right, improve=0.02977419, (0 missing)
## prdl.descr.my.fctriPadmini#0 < 0.5 to the left, improve=0.01380903, (0 missing)
## Surrogate splits:
## D.ratio.weight.sum.wrds.n < 0.5190839 to the left, agree=0.697, adj=0.148, (0 split)
## condition.fctrNew other (see details) < 0.5 to the left, agree=0.671, adj=0.074, (0 split)
## prdl.descr.my.fctriPad4#0 < 0.5 to the left, agree=0.671, adj=0.074, (0 split)
## prdl.descr.my.fctriPadAir2#1 < 0.5 to the left, agree=0.671, adj=0.074, (0 split)
## color.fctrGold < 0.5 to the left, agree=0.658, adj=0.037, (0 split)
##
## Node number 6: 9 observations
## mean=0.5766871, MSE=2.237308
##
## Node number 7: 384 observations, complexity param=0.016192
## mean=1.885069, MSE=0.2877274
## left son=14 (42 obs) right son=15 (342 obs)
## Primary splits:
## condition.fctrFor parts or not working < 0.5 to the right, improve=0.07996179, (0 missing)
## prdl.descr.my.fctriPadAir2#0 < 0.5 to the left, improve=0.05156989, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.03599377, (0 missing)
## color.fctrSpace Gray < 0.5 to the left, improve=0.02743014, (0 missing)
## prdl.descr.my.fctriPad1#0 < 0.5 to the right, improve=0.02288077, (0 missing)
##
## Node number 10: 49 observations, complexity param=0.005222924
## mean=1.747408, MSE=0.2419478
## left son=20 (11 obs) right son=21 (38 obs)
## Primary splits:
## D.ratio.weight.sum.wrds.n < 0.3351285 to the right, improve=0.264669000, (0 missing)
## prdl.descr.my.fctriPad2#1 < 0.5 to the right, improve=0.043167320, (0 missing)
## color.fctrWhite < 0.5 to the right, improve=0.043159730, (0 missing)
## color.fctrUnknown < 0.5 to the right, improve=0.011507380, (0 missing)
## cellular.fctr1 < 0.5 to the left, improve=0.008839971, (0 missing)
##
## Node number 11: 27 observations
## mean=2.131023, MSE=0.03832866
##
## Node number 14: 42 observations
## mean=1.452236, MSE=0.2652926
##
## Node number 15: 342 observations, complexity param=0.008081388
## mean=1.938224, MSE=0.2646499
## left son=30 (313 obs) right son=31 (29 obs)
## Primary splits:
## prdl.descr.my.fctriPadAir2#0 < 0.5 to the left, improve=0.04871724, (0 missing)
## condition.fctrNew < 0.5 to the left, improve=0.03045419, (0 missing)
## prdl.descr.my.fctriPad1#0 < 0.5 to the right, improve=0.03012162, (0 missing)
## color.fctrSpace Gray < 0.5 to the left, improve=0.02808065, (0 missing)
## prdl.descr.my.fctriPad1#1 < 0.5 to the right, improve=0.02112807, (0 missing)
## Surrogate splits:
## color.fctrGold < 0.5 to the left, agree=0.921, adj=0.069, (0 split)
## cellular.fctr1:carrier.fctrSprint < 0.5 to the left, agree=0.918, adj=0.034, (0 split)
##
## Node number 20: 11 observations
## mean=1.277073, MSE=0.1392125
##
## Node number 21: 38 observations
## mean=1.883558, MSE=0.1891141
##
## Node number 30: 313 observations, complexity param=0.004447308
## mean=1.903661, MSE=0.231944
## left son=60 (291 obs) right son=61 (22 obs)
## Primary splits:
## condition.fctrNew < 0.5 to the left, improve=0.03342445, (0 missing)
## color.fctrSpace Gray < 0.5 to the left, improve=0.02969764, (0 missing)
## prdl.descr.my.fctriPad1#0 < 0.5 to the right, improve=0.02818294, (0 missing)
## prdl.descr.my.fctriPad1#1 < 0.5 to the right, improve=0.02102236, (0 missing)
## color.fctrUnknown < 0.5 to the right, improve=0.01953239, (0 missing)
## Surrogate splits:
## color.fctrGold < 0.5 to the left, agree=0.933, adj=0.045, (0 split)
##
## Node number 31: 29 observations
## mean=2.311259, MSE=0.465599
##
## Node number 60: 291 observations, complexity param=0.003385734
## mean=1.879452, MSE=0.2346886
## left son=120 (39 obs) right son=121 (252 obs)
## Primary splits:
## prdl.descr.my.fctriPad1#0 < 0.5 to the right, improve=0.02382894, (0 missing)
## cellular.fctrUnknown < 0.5 to the right, improve=0.02182334, (0 missing)
## cellular.fctrUnknown:carrier.fctrUnknown < 0.5 to the right, improve=0.02182334, (0 missing)
## startprice.dgt2.is9 < 0.5 to the left, improve=0.02181721, (0 missing)
## prdl.descr.my.fctriPad1#1 < 0.5 to the right, improve=0.01877006, (0 missing)
##
## Node number 61: 22 observations
## mean=2.223889, MSE=0.08534252
##
## Node number 120: 39 observations
## mean=1.689359, MSE=0.03637788
##
## Node number 121: 252 observations, complexity param=0.003385734
## mean=1.908871, MSE=0.2589217
## left son=242 (16 obs) right son=243 (236 obs)
## Primary splits:
## cellular.fctrUnknown < 0.5 to the right, improve=0.02784735, (0 missing)
## cellular.fctrUnknown:carrier.fctrUnknown < 0.5 to the right, improve=0.02784735, (0 missing)
## prdl.descr.my.fctriPad1#1 < 0.5 to the right, improve=0.02462428, (0 missing)
## color.fctrUnknown < 0.5 to the right, improve=0.01900132, (0 missing)
## startprice.dgt2.is9 < 0.5 to the left, improve=0.01721968, (0 missing)
## Surrogate splits:
## cellular.fctrUnknown:carrier.fctrUnknown < 0.5 to the right, agree=1, adj=1, (0 split)
##
## Node number 242: 16 observations
## mean=1.582755, MSE=0.2216833
##
## Node number 243: 236 observations, complexity param=0.003385734
## mean=1.93098, MSE=0.2537472
## left son=486 (15 obs) right son=487 (221 obs)
## Primary splits:
## prdl.descr.my.fctriPad1#1 < 0.5 to the right, improve=0.03502843, (0 missing)
## color.fctrUnknown < 0.5 to the right, improve=0.02026642, (0 missing)
## prdl.descr.my.fctriPadmini2#1 < 0.5 to the left, improve=0.01558276, (0 missing)
## startprice.dgt2.is9 < 0.5 to the left, improve=0.01463351, (0 missing)
## startprice.dgt1.is9 < 0.5 to the left, improve=0.01186002, (0 missing)
##
## Node number 486: 15 observations
## mean=1.569103, MSE=0.2156864
##
## Node number 487: 221 observations
## mean=1.955542, MSE=0.2468389
##
## n= 621
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 621 545.625400 1.42576800
## 2) startprice.dcm2.is9>=0.5 228 202.638500 0.68572370
## 4) startprice.dgt1.is9< 0.5 152 23.581640 0.08673919 *
## 5) startprice.dgt1.is9>=0.5 76 15.452070 1.88369300
## 10) startprice.dgt2.is9< 0.5 49 11.855440 1.74740800
## 20) D.ratio.weight.sum.wrds.n>=0.3351285 11 1.531337 1.27707300 *
## 21) D.ratio.weight.sum.wrds.n< 0.3351285 38 7.186337 1.88355800 *
## 11) startprice.dgt2.is9>=0.5 27 1.034874 2.13102300 *
## 3) startprice.dcm2.is9< 0.5 393 145.677000 1.85510600
## 6) D.ratio.weight.sum.wrds.n>=1.958818 9 20.135770 0.57668710 *
## 7) D.ratio.weight.sum.wrds.n< 1.958818 384 110.487300 1.88506900
## 14) condition.fctrFor parts or not working>=0.5 42 11.142290 1.45223600 *
## 15) condition.fctrFor parts or not working< 0.5 342 90.510260 1.93822400
## 30) prdl.descr.my.fctriPadAir2#0< 0.5 313 72.598480 1.90366100
## 60) condition.fctrNew< 0.5 291 68.294380 1.87945200
## 120) prdl.descr.my.fctriPad1#0>=0.5 39 1.418737 1.68935900 *
## 121) prdl.descr.my.fctriPad1#0< 0.5 252 65.248260 1.90887100
## 242) cellular.fctrUnknown>=0.5 16 3.546933 1.58275500 *
## 243) cellular.fctrUnknown< 0.5 236 59.884340 1.93098000
## 486) prdl.descr.my.fctriPad1#1>=0.5 15 3.235296 1.56910300 *
## 487) prdl.descr.my.fctriPad1#1< 0.5 221 54.551390 1.95554200 *
## 61) condition.fctrNew>=0.5 22 1.877535 2.22388900 *
## 31) prdl.descr.my.fctriPadAir2#0>=0.5 29 13.502370 2.31125900 *
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.rpart
## feats
## 1 startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 1.5 0.037
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.7383837 0.5186586 NA 0.2105739 1.032426
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 NA 0.693594 0.06786912 0.0696927
## label step_major step_minor label_minor bgn end
## 5 fit.models_1_RFE.X 2 3 rpart 166.663 170.463
## 6 fit.models_1_RFE.X 2 4 gbm 170.464 NA
## elapsed
## 5 3.801
## 6 NA
## [1] "fitting model: RFE.X.gbm"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr"## Loading required package: gbm
## Loading required package: splines
## Loaded gbm 2.1.1## Aggregating results
## Fitting final model on full training set## Warning in gbm.fit(x = structure(c(0.297019563193329, 0, 0, 0, 0,
## 0.32797677549543, : variable 37: cellular.fctr1:carrier.fctrNone has no
## variation.## Warning in gbm.fit(x = structure(c(0.297019563193329, 0, 0, 0, 0,
## 0.32797677549543, : variable 38: cellular.fctrUnknown:carrier.fctrNone has
## no variation.## Warning in gbm.fit(x = structure(c(0.297019563193329, 0, 0, 0, 0,
## 0.32797677549543, : variable 39: cellular.fctr0:carrier.fctrOther has no
## variation.## Warning in gbm.fit(x = structure(c(0.297019563193329, 0, 0, 0, 0,
## 0.32797677549543, : variable 41: cellular.fctrUnknown:carrier.fctrOther has
## no variation.## Warning in gbm.fit(x = structure(c(0.297019563193329, 0, 0, 0, 0,
## 0.32797677549543, : variable 42: cellular.fctr0:carrier.fctrSprint has no
## variation.## Warning in gbm.fit(x = structure(c(0.297019563193329, 0, 0, 0, 0,
## 0.32797677549543, : variable 44: cellular.fctrUnknown:carrier.fctrSprint
## has no variation.## Warning in gbm.fit(x = structure(c(0.297019563193329, 0, 0, 0, 0,
## 0.32797677549543, : variable 45: cellular.fctr0:carrier.fctrT-Mobile has no
## variation.## Warning in gbm.fit(x = structure(c(0.297019563193329, 0, 0, 0, 0,
## 0.32797677549543, : variable 47: cellular.fctrUnknown:carrier.fctrT-Mobile
## has no variation.## Warning in gbm.fit(x = structure(c(0.297019563193329, 0, 0, 0, 0,
## 0.32797677549543, : variable 48: cellular.fctr0:carrier.fctrUnknown has no
## variation.## Warning in gbm.fit(x = structure(c(0.297019563193329, 0, 0, 0, 0,
## 0.32797677549543, : variable 51: cellular.fctr0:carrier.fctrVerizon has no
## variation.## Warning in gbm.fit(x = structure(c(0.297019563193329, 0, 0, 0, 0,
## 0.32797677549543, : variable 53: cellular.fctrUnknown:carrier.fctrVerizon
## has no variation.
## Iter TrainDeviance ValidDeviance StepSize Improve
## 1 0.8301 nan 0.0400 0.0479
## 2 0.7850 nan 0.0400 0.0425
## 3 0.7424 nan 0.0400 0.0425
## 4 0.7050 nan 0.0400 0.0360
## 5 0.6685 nan 0.0400 0.0372
## 6 0.6365 nan 0.0400 0.0314
## 7 0.6074 nan 0.0400 0.0290
## 8 0.5801 nan 0.0400 0.0288
## 9 0.5558 nan 0.0400 0.0246
## 10 0.5316 nan 0.0400 0.0224
## 20 0.3718 nan 0.0400 0.0078
## 40 0.2621 nan 0.0400 0.0016
## 60 0.2329 nan 0.0400 0.0004
## 80 0.2199 nan 0.0400 -0.0003
## 100 0.2108 nan 0.0400 -0.0008
## 120 0.2038 nan 0.0400 -0.0005
## 140 0.1977 nan 0.0400 -0.0007
## 160 0.1935 nan 0.0400 -0.0009
## 180 0.1895 nan 0.0400 -0.0007
## 200 0.1854 nan 0.0400 -0.0006
## var
## startprice.dcm2.is9 startprice.dcm2.is9
## startprice.dgt1.is9 startprice.dgt1.is9
## D.ratio.weight.sum.wrds.n D.ratio.weight.sum.wrds.n
## startprice.dcm1.is9 startprice.dcm1.is9
## condition.fctrFor parts or not working condition.fctrFor parts or not working
## prdl.descr.my.fctriPadAir2#0 prdl.descr.my.fctriPadAir2#0
## startprice.dgt2.is9 startprice.dgt2.is9
## cellular.fctr1:carrier.fctrVerizon cellular.fctr1:carrier.fctrVerizon
## color.fctrUnknown color.fctrUnknown
## color.fctrSpace Gray color.fctrSpace Gray
## condition.fctrNew condition.fctrNew
## prdl.descr.my.fctriPadAir#0 prdl.descr.my.fctriPadAir#0
## cellular.fctr0:carrier.fctrNone cellular.fctr0:carrier.fctrNone
## prdl.descr.my.fctriPad1#1 prdl.descr.my.fctriPad1#1
## prdl.descr.my.fctriPad3#0 prdl.descr.my.fctriPad3#0
## prdl.descr.my.fctriPad1#0 prdl.descr.my.fctriPad1#0
## cellular.fctr1 cellular.fctr1
## cellular.fctr1:carrier.fctrUnknown cellular.fctr1:carrier.fctrUnknown
## prdl.descr.my.fctriPadAir#1 prdl.descr.my.fctriPadAir#1
## cellular.fctrUnknown cellular.fctrUnknown
## prdl.descr.my.fctriPad2#0 prdl.descr.my.fctriPad2#0
## color.fctrWhite color.fctrWhite
## prdl.descr.my.fctriPadmini#1 prdl.descr.my.fctriPadmini#1
## prdl.descr.my.fctriPad4#0 prdl.descr.my.fctriPad4#0
## prdl.descr.my.fctrUnknown#1 prdl.descr.my.fctrUnknown#1
## prdl.descr.my.fctriPad3#1 prdl.descr.my.fctriPad3#1
## color.fctrGold color.fctrGold
## prdl.descr.my.fctriPad2#1 prdl.descr.my.fctriPad2#1
## prdl.descr.my.fctriPadmini#0 prdl.descr.my.fctriPadmini#0
## prdl.descr.my.fctriPad4#1 prdl.descr.my.fctriPad4#1
## condition.fctrNew other (see details) condition.fctrNew other (see details)
## prdl.descr.my.fctriPadmini2#0 prdl.descr.my.fctriPadmini2#0
## prdl.descr.my.fctriPadAir2#1 prdl.descr.my.fctriPadAir2#1
## condition.fctrSeller refurbished condition.fctrSeller refurbished
## prdl.descr.my.fctriPadmini3#0 prdl.descr.my.fctriPadmini3#0
## condition.fctrManufacturer refurbished condition.fctrManufacturer refurbished
## prdl.descr.my.fctriPadmini2#1 prdl.descr.my.fctriPadmini2#1
## prdl.descr.my.fctriPadmini3#1 prdl.descr.my.fctriPadmini3#1
## cellular.fctr1:carrier.fctrNone cellular.fctr1:carrier.fctrNone
## cellular.fctrUnknown:carrier.fctrNone cellular.fctrUnknown:carrier.fctrNone
## cellular.fctr0:carrier.fctrOther cellular.fctr0:carrier.fctrOther
## cellular.fctr1:carrier.fctrOther cellular.fctr1:carrier.fctrOther
## cellular.fctrUnknown:carrier.fctrOther cellular.fctrUnknown:carrier.fctrOther
## cellular.fctr0:carrier.fctrSprint cellular.fctr0:carrier.fctrSprint
## cellular.fctr1:carrier.fctrSprint cellular.fctr1:carrier.fctrSprint
## cellular.fctrUnknown:carrier.fctrSprint cellular.fctrUnknown:carrier.fctrSprint
## cellular.fctr0:carrier.fctrT-Mobile cellular.fctr0:carrier.fctrT-Mobile
## cellular.fctr1:carrier.fctrT-Mobile cellular.fctr1:carrier.fctrT-Mobile
## cellular.fctrUnknown:carrier.fctrT-Mobile cellular.fctrUnknown:carrier.fctrT-Mobile
## cellular.fctr0:carrier.fctrUnknown cellular.fctr0:carrier.fctrUnknown
## cellular.fctrUnknown:carrier.fctrUnknown cellular.fctrUnknown:carrier.fctrUnknown
## cellular.fctr0:carrier.fctrVerizon cellular.fctr0:carrier.fctrVerizon
## cellular.fctrUnknown:carrier.fctrVerizon cellular.fctrUnknown:carrier.fctrVerizon
## rel.inf
## startprice.dcm2.is9 33.055728805
## startprice.dgt1.is9 31.066657032
## D.ratio.weight.sum.wrds.n 11.725420604
## startprice.dcm1.is9 5.980320966
## condition.fctrFor parts or not working 2.490000716
## prdl.descr.my.fctriPadAir2#0 1.938189924
## startprice.dgt2.is9 1.643631949
## cellular.fctr1:carrier.fctrVerizon 1.181546922
## color.fctrUnknown 1.165812790
## color.fctrSpace Gray 1.100401128
## condition.fctrNew 0.781886870
## prdl.descr.my.fctriPadAir#0 0.707465915
## cellular.fctr0:carrier.fctrNone 0.695414877
## prdl.descr.my.fctriPad1#1 0.648666091
## prdl.descr.my.fctriPad3#0 0.638982830
## prdl.descr.my.fctriPad1#0 0.606285679
## cellular.fctr1 0.590109989
## cellular.fctr1:carrier.fctrUnknown 0.555979892
## prdl.descr.my.fctriPadAir#1 0.550646328
## cellular.fctrUnknown 0.525378510
## prdl.descr.my.fctriPad2#0 0.378570498
## color.fctrWhite 0.330822416
## prdl.descr.my.fctriPadmini#1 0.303892600
## prdl.descr.my.fctriPad4#0 0.260094518
## prdl.descr.my.fctrUnknown#1 0.220034995
## prdl.descr.my.fctriPad3#1 0.181055054
## color.fctrGold 0.156117970
## prdl.descr.my.fctriPad2#1 0.136689500
## prdl.descr.my.fctriPadmini#0 0.091638230
## prdl.descr.my.fctriPad4#1 0.089939138
## condition.fctrNew other (see details) 0.082611288
## prdl.descr.my.fctriPadmini2#0 0.069761116
## prdl.descr.my.fctriPadAir2#1 0.031711842
## condition.fctrSeller refurbished 0.009970532
## prdl.descr.my.fctriPadmini3#0 0.008562487
## condition.fctrManufacturer refurbished 0.000000000
## prdl.descr.my.fctriPadmini2#1 0.000000000
## prdl.descr.my.fctriPadmini3#1 0.000000000
## cellular.fctr1:carrier.fctrNone 0.000000000
## cellular.fctrUnknown:carrier.fctrNone 0.000000000
## cellular.fctr0:carrier.fctrOther 0.000000000
## cellular.fctr1:carrier.fctrOther 0.000000000
## cellular.fctrUnknown:carrier.fctrOther 0.000000000
## cellular.fctr0:carrier.fctrSprint 0.000000000
## cellular.fctr1:carrier.fctrSprint 0.000000000
## cellular.fctrUnknown:carrier.fctrSprint 0.000000000
## cellular.fctr0:carrier.fctrT-Mobile 0.000000000
## cellular.fctr1:carrier.fctrT-Mobile 0.000000000
## cellular.fctrUnknown:carrier.fctrT-Mobile 0.000000000
## cellular.fctr0:carrier.fctrUnknown 0.000000000
## cellular.fctrUnknown:carrier.fctrUnknown 0.000000000
## cellular.fctr0:carrier.fctrVerizon 0.000000000
## cellular.fctrUnknown:carrier.fctrVerizon 0.000000000
## [1] " calling mypredict_mdl for fit:"## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.gbm
## feats
## 1 startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 2.94 0.693
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.7890316 0.5057258 0.7693114 0.2407367 1.012759
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1656603 0.7075303 0.07012577 0.07101878
## label step_major step_minor label_minor bgn end
## 6 fit.models_1_RFE.X 2 4 gbm 170.464 175.314
## 7 fit.models_1_RFE.X 2 5 rf 175.314 NA
## elapsed
## 6 4.85
## 7 NA
## [1] "fitting model: RFE.X.rf"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr"## Loading required package: randomForest
## randomForest 4.6-12
## Type rfNews() to see new features/changes/bug fixes.
##
## Attaching package: 'randomForest'
##
## The following object is masked from 'package:dplyr':
##
## combine
##
## The following object is masked from 'package:gdata':
##
## combine
##
## The following object is masked from 'package:ggplot2':
##
## margin
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 14 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 621 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 621 -none- numeric
## importance 53 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 621 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 53 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.rf
## feats
## 1 startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 9.204 1.755
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8971826 0.4813558 0.8875718 0.2383591 1.014353
## max.Adj.R.sq.OOB max.Rsquared.fit
## 1 0.1630476 0.7362883
## label step_major step_minor label_minor bgn end
## 7 fit.models_1_RFE.X 2 5 rf 175.314 186.731
## 8 fit.models_1_RFE.X 2 6 nnet 186.732 NA
## elapsed
## 7 11.417
## 8 NA
## [1] "fitting model: RFE.X.nnet"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr"## Loading required package: nnet
## Aggregating results
## Selecting tuning parameters
## Fitting size = 7, decay = 0.1 on full training set
## # weights: 386
## initial value 1374.446578
## iter 10 value 660.560608
## iter 20 value 553.325660
## iter 30 value 533.142629
## iter 40 value 530.041081
## iter 50 value 528.837500
## iter 60 value 528.161322
## iter 70 value 527.964261
## iter 80 value 527.901471
## iter 90 value 527.888683
## iter 100 value 527.864615
## final value 527.864615
## stopped after 100 iterations## Loading required package: NeuralNetTools## Warning: replacing previous import by 'scales::alpha' when loading
## 'NeuralNetTools'
## a 53-7-1 network with 386 weights
## options were - decay=0.1
## b->h1 i1->h1 i2->h1 i3->h1 i4->h1 i5->h1 i6->h1 i7->h1 i8->h1
## -0.87 0.68 -0.03 -0.01 -0.51 -0.74 -0.16 -0.13 1.32
## i9->h1 i10->h1 i11->h1 i12->h1 i13->h1 i14->h1 i15->h1 i16->h1 i17->h1
## 0.01 -0.09 -0.14 0.15 0.18 0.82 -0.13 0.20 -0.10
## i18->h1 i19->h1 i20->h1 i21->h1 i22->h1 i23->h1 i24->h1 i25->h1 i26->h1
## -0.04 -0.08 0.11 -0.20 -0.40 -0.73 0.40 -0.19 0.35
## i27->h1 i28->h1 i29->h1 i30->h1 i31->h1 i32->h1 i33->h1 i34->h1 i35->h1
## -0.06 0.10 -1.19 0.36 0.03 1.29 1.00 -2.02 -0.55
## i36->h1 i37->h1 i38->h1 i39->h1 i40->h1 i41->h1 i42->h1 i43->h1 i44->h1
## -0.83 0.00 0.00 0.00 -0.76 0.00 0.00 0.09 0.00
## i45->h1 i46->h1 i47->h1 i48->h1 i49->h1 i50->h1 i51->h1 i52->h1 i53->h1
## 0.00 0.40 0.00 0.00 0.46 -0.01 0.00 -0.22 0.00
## b->h2 i1->h2 i2->h2 i3->h2 i4->h2 i5->h2 i6->h2 i7->h2 i8->h2
## 0.72 -0.35 0.27 0.19 0.26 0.28 -0.05 -0.04 0.34
## i9->h2 i10->h2 i11->h2 i12->h2 i13->h2 i14->h2 i15->h2 i16->h2 i17->h2
## -0.15 0.07 0.07 -0.16 0.32 0.34 -0.03 -0.01 0.11
## i18->h2 i19->h2 i20->h2 i21->h2 i22->h2 i23->h2 i24->h2 i25->h2 i26->h2
## -0.13 -0.14 -0.02 -0.15 0.12 0.23 -0.29 0.10 -0.13
## i27->h2 i28->h2 i29->h2 i30->h2 i31->h2 i32->h2 i33->h2 i34->h2 i35->h2
## -0.11 -0.04 0.80 -0.20 -0.02 -0.54 -1.37 1.53 0.46
## i36->h2 i37->h2 i38->h2 i39->h2 i40->h2 i41->h2 i42->h2 i43->h2 i44->h2
## 0.25 0.00 0.00 0.00 0.51 0.00 0.00 -0.07 0.00
## i45->h2 i46->h2 i47->h2 i48->h2 i49->h2 i50->h2 i51->h2 i52->h2 i53->h2
## 0.00 -0.31 0.00 0.00 0.29 0.19 0.00 -0.10 0.00
## b->h3 i1->h3 i2->h3 i3->h3 i4->h3 i5->h3 i6->h3 i7->h3 i8->h3
## 0.62 -0.32 0.24 0.16 0.21 0.28 -0.07 -0.03 0.29
## i9->h3 i10->h3 i11->h3 i12->h3 i13->h3 i14->h3 i15->h3 i16->h3 i17->h3
## -0.13 0.05 0.06 -0.14 0.27 0.30 -0.02 -0.02 0.10
## i18->h3 i19->h3 i20->h3 i21->h3 i22->h3 i23->h3 i24->h3 i25->h3 i26->h3
## -0.10 -0.13 -0.02 -0.13 0.12 0.24 -0.25 0.08 -0.12
## i27->h3 i28->h3 i29->h3 i30->h3 i31->h3 i32->h3 i33->h3 i34->h3 i35->h3
## -0.09 -0.03 0.67 -0.18 -0.02 -0.49 -1.19 1.30 0.39
## i36->h3 i37->h3 i38->h3 i39->h3 i40->h3 i41->h3 i42->h3 i43->h3 i44->h3
## 0.22 0.00 0.00 0.00 0.43 0.00 0.00 -0.06 0.00
## i45->h3 i46->h3 i47->h3 i48->h3 i49->h3 i50->h3 i51->h3 i52->h3 i53->h3
## 0.00 -0.26 0.00 0.00 0.25 0.16 0.00 -0.08 0.00
## b->h4 i1->h4 i2->h4 i3->h4 i4->h4 i5->h4 i6->h4 i7->h4 i8->h4
## 0.82 -0.38 0.31 0.21 0.29 0.33 -0.05 -0.06 0.38
## i9->h4 i10->h4 i11->h4 i12->h4 i13->h4 i14->h4 i15->h4 i16->h4 i17->h4
## -0.17 0.08 0.09 -0.18 0.35 0.39 -0.04 -0.01 0.13
## i18->h4 i19->h4 i20->h4 i21->h4 i22->h4 i23->h4 i24->h4 i25->h4 i26->h4
## -0.15 -0.16 -0.02 -0.16 0.14 0.26 -0.33 0.12 -0.15
## i27->h4 i28->h4 i29->h4 i30->h4 i31->h4 i32->h4 i33->h4 i34->h4 i35->h4
## -0.15 -0.04 0.93 -0.23 -0.02 -0.59 -1.52 1.70 0.51
## i36->h4 i37->h4 i38->h4 i39->h4 i40->h4 i41->h4 i42->h4 i43->h4 i44->h4
## 0.30 0.00 0.00 0.00 0.58 0.00 0.00 -0.07 0.00
## i45->h4 i46->h4 i47->h4 i48->h4 i49->h4 i50->h4 i51->h4 i52->h4 i53->h4
## 0.00 -0.36 0.00 0.00 0.33 0.21 0.00 -0.11 0.00
## b->h5 i1->h5 i2->h5 i3->h5 i4->h5 i5->h5 i6->h5 i7->h5 i8->h5
## 0.55 -0.31 0.21 0.15 0.17 0.20 -0.06 -0.01 0.26
## i9->h5 i10->h5 i11->h5 i12->h5 i13->h5 i14->h5 i15->h5 i16->h5 i17->h5
## -0.11 0.05 0.06 -0.12 0.24 0.25 -0.01 -0.01 0.09
## i18->h5 i19->h5 i20->h5 i21->h5 i22->h5 i23->h5 i24->h5 i25->h5 i26->h5
## -0.09 -0.11 -0.02 -0.12 0.09 0.17 -0.20 0.07 -0.09
## i27->h5 i28->h5 i29->h5 i30->h5 i31->h5 i32->h5 i33->h5 i34->h5 i35->h5
## -0.06 -0.03 0.57 -0.16 -0.01 -0.48 -1.09 1.16 0.35
## i36->h5 i37->h5 i38->h5 i39->h5 i40->h5 i41->h5 i42->h5 i43->h5 i44->h5
## 0.19 0.00 0.00 0.00 0.36 0.00 0.00 -0.05 0.00
## i45->h5 i46->h5 i47->h5 i48->h5 i49->h5 i50->h5 i51->h5 i52->h5 i53->h5
## 0.00 -0.23 0.00 0.00 0.23 0.15 0.00 -0.07 0.00
## b->h6 i1->h6 i2->h6 i3->h6 i4->h6 i5->h6 i6->h6 i7->h6 i8->h6
## 0.68 -0.34 0.26 0.18 0.24 0.24 -0.04 -0.03 0.33
## i9->h6 i10->h6 i11->h6 i12->h6 i13->h6 i14->h6 i15->h6 i16->h6 i17->h6
## -0.14 0.06 0.07 -0.15 0.30 0.32 -0.02 -0.01 0.11
## i18->h6 i19->h6 i20->h6 i21->h6 i22->h6 i23->h6 i24->h6 i25->h6 i26->h6
## -0.12 -0.12 -0.02 -0.15 0.10 0.20 -0.26 0.09 -0.11
## i27->h6 i28->h6 i29->h6 i30->h6 i31->h6 i32->h6 i33->h6 i34->h6 i35->h6
## -0.09 -0.04 0.74 -0.19 -0.02 -0.52 -1.30 1.44 0.43
## i36->h6 i37->h6 i38->h6 i39->h6 i40->h6 i41->h6 i42->h6 i43->h6 i44->h6
## 0.23 0.00 0.00 0.00 0.48 0.00 0.00 -0.06 0.00
## i45->h6 i46->h6 i47->h6 i48->h6 i49->h6 i50->h6 i51->h6 i52->h6 i53->h6
## 0.00 -0.29 0.00 0.00 0.27 0.18 0.00 -0.10 0.00
## b->h7 i1->h7 i2->h7 i3->h7 i4->h7 i5->h7 i6->h7 i7->h7 i8->h7
## -1.01 0.43 -0.34 -0.26 -0.35 -0.63 0.12 0.11 -0.43
## i9->h7 i10->h7 i11->h7 i12->h7 i13->h7 i14->h7 i15->h7 i16->h7 i17->h7
## 0.22 -0.10 -0.11 0.24 -0.41 -0.48 0.06 0.00 -0.14
## i18->h7 i19->h7 i20->h7 i21->h7 i22->h7 i23->h7 i24->h7 i25->h7 i26->h7
## 0.19 0.24 0.03 0.17 -0.25 -0.50 0.46 -0.14 0.23
## i27->h7 i28->h7 i29->h7 i30->h7 i31->h7 i32->h7 i33->h7 i34->h7 i35->h7
## 0.29 0.05 -1.21 0.32 0.03 0.66 1.80 -2.05 -0.62
## i36->h7 i37->h7 i38->h7 i39->h7 i40->h7 i41->h7 i42->h7 i43->h7 i44->h7
## -0.41 0.00 0.00 0.00 -0.73 0.00 0.00 0.09 0.00
## i45->h7 i46->h7 i47->h7 i48->h7 i49->h7 i50->h7 i51->h7 i52->h7 i53->h7
## 0.00 0.47 0.00 0.00 -0.41 -0.26 0.00 0.10 0.00
## b->o h1->o h2->o h3->o h4->o h5->o h6->o h7->o
## -0.18 -4.15 2.80 2.38 3.20 2.09 2.62 -4.14
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.nnet
## feats
## 1 startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 13.548 0.336
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.359983 0.9287422 0.3001577 0.181812 1.31299
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1009091 0.6532849 0.02330897 0.07391302
## label step_major step_minor label_minor bgn end
## 8 fit.models_1_RFE.X 2 6 nnet 186.732 203.366
## 9 fit.models_1_RFE.X 2 7 avNNet 203.367 NA
## elapsed
## 8 16.634
## 9 NA
## [1] "fitting model: RFE.X.avNNet"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr"## Warning in nominalTrainWorkflow(x = x, y = y, wts = weights, info =
## trainInfo, : There were missing values in resampled performance measures.
## Aggregating results
## Selecting tuning parameters
## Fitting size = 9, decay = 0.1, bag = FALSE on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: size## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: decay## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: bag## Length Class Mode
## model 5 -none- list
## repeats 1 -none- numeric
## bag 1 -none- logical
## names 53 -none- character
## terms 3 terms call
## coefnames 53 -none- character
## xlevels 0 -none- list
## xNames 53 -none- character
## problemType 1 -none- character
## tuneValue 3 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.avNNet
## feats
## 1 startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 23.452 0.576
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.3592513 0.9285701 0.3487472 0.1801205 1.312557
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1659603 0.6527267 0.02312541 0.07095136
## label step_major step_minor label_minor bgn end
## 9 fit.models_1_RFE.X 2 7 avNNet 203.367 228.918
## 10 fit.models_1_RFE.X 2 8 svmLinear 228.919 NA
## elapsed
## 9 25.551
## 10 NA
## [1] "fitting model: RFE.X.svmLinear"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr"
## [1] "User-specified Tuning Grid Length: 5"
## C
## 1 0.01
## 2 0.05
## 3 0.10
## 4 0.50
## 5 1.00## Loading required package: kernlab
##
## Attaching package: 'kernlab'
##
## The following object is masked from 'package:ggplot2':
##
## alpha## Aggregating results
## Selecting tuning parameters
## Fitting C = 0.05 on full training set## Warning in .local(x, ...): Variable(s) `' constant. Cannot scale data.
## Length Class Mode
## 1 ksvm S4
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.svmLinear
## feats
## 1 startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 2.62 0.065
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.5759659 0.6324697 0.5690146 0.124059 1.08565
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1089305 0.5621811 0.05924106 0.06419741
## label step_major step_minor label_minor bgn end
## 10 fit.models_1_RFE.X 2 8 svmLinear 228.919 233.266
## 11 fit.models_1_RFE.X 2 9 svmLinear2 233.266 NA
## elapsed
## 10 4.347
## 11 NA
## [1] "fitting model: RFE.X.svmLinear2"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr"## Loading required package: e1071## Aggregating results
## Selecting tuning parameters
## Fitting cost = 0.0625 on full training set## Warning in svm.default(x = as.matrix(x), y = y, kernel = "linear",
## cost = param$cost, : Variable(s) 'cellular.fctr1.carrier.fctrNone'
## and 'cellular.fctrUnknown.carrier.fctrNone'
## and 'cellular.fctr0.carrier.fctrOther' and
## 'cellular.fctrUnknown.carrier.fctrOther'
## and 'cellular.fctr0.carrier.fctrSprint' and
## 'cellular.fctrUnknown.carrier.fctrSprint'
## and 'cellular.fctr0.carrier.fctrT.Mobile' and
## 'cellular.fctrUnknown.carrier.fctrT.Mobile'
## and 'cellular.fctr0.carrier.fctrUnknown'
## and 'cellular.fctr0.carrier.fctrVerizon' and
## 'cellular.fctrUnknown.carrier.fctrVerizon' constant. Cannot scale data.
##
## Call:
## svm.default(x = as.matrix(x), y = y, kernel = "linear", cost = param$cost)
##
##
## Parameters:
## SVM-Type: eps-regression
## SVM-Kernel: linear
## cost: 0.0625
## gamma: 0.01886792
## epsilon: 0.1
##
##
## Number of Support Vectors: 482
##
##
##
##
##
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.svmLinear2
## feats
## 1 startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 2.16 0.084
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.5772012 0.6335311 0.57027 0.1240085 1.084196
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1088791 0.5656896 0.05951189 0.0628137
## label step_major step_minor label_minor bgn end
## 11 fit.models_1_RFE.X 2 9 svmLinear2 233.266 237.212
## 12 fit.models_1_RFE.X 2 10 earth 237.212 NA
## elapsed
## 11 3.946
## 12 NA
## [1] "fitting model: RFE.X.earth"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr"## Loading required package: earth
## Loading required package: plotmo
## Loading required package: plotrix
## Loading required package: TeachingDemos
## Aggregating results
## Selecting tuning parameters
## Fitting nprune = 11, degree = 1 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: degree
## Call: earth(x=matrix[621,53], y=c(-0.0044,2.243...), keepxy=TRUE,
## degree=1, nprune=11)
##
## coefficients
## (Intercept) 1.7792662
## condition.fctrFor parts or not working -0.2697048
## prdl.descr.my.fctriPad1#1 -0.2494019
## prdl.descr.my.fctriPadAir2#0 0.4211987
## startprice.dcm1.is9 -0.8974488
## startprice.dcm2.is9 -0.4818093
## startprice.dgt1.is9 1.0410520
## cellular.fctr1:carrier.fctrOther 1.2567898
## cellular.fctr1:carrier.fctrSprint -0.5410732
## h(D.ratio.weight.sum.wrds.n-1.89738) -0.6870302
##
## Selected 10 of 24 terms, and 9 of 53 predictors
## Termination condition: RSq changed by less than 0.001 at 24 terms
## Importance: startprice.dcm1.is9, startprice.dgt1.is9, ...
## Number of terms at each degree of interaction: 1 9 (additive model)
## GCV 0.3546265 RSS 206.9534 GRSq 0.597683 RSq 0.6207042
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.earth
## feats
## 1 startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 3.19 0.201
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.6207042 0.6332435 0.5852498 0.1618643 1.063801
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.07898899 0.5522804 0.05384071 0.06014198
## label step_major step_minor label_minor bgn end
## 12 fit.models_1_RFE.X 2 10 earth 237.212 242.93
## 13 fit.models_1_RFE.X 2 11 bagEarth 242.931 NA
## elapsed
## 12 5.718
## 13 NA
## [1] "fitting model: RFE.X.bagEarth"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr"
## [1] "User-specified Tuning Grid Length: 1"
## nprune degree
## 1 256 2
## + : nprune=256, degree=2
## - : nprune=256, degree=2
## Aggregating results
## Fitting final model on full training set
##
## Call:
## bagEarth.default(x = structure(c(0.297019563193329, 0, 0, 0, 0, 0.32797677549543, 0, 2.24354782376543, 0, 0, 0, 0.297019563193329, 0.297019563193329, 0.322380094217495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.20386459491477, 0, 0.33128769888252, 0.382908936860708, 0, 0.785133026809279, 0, 0, 0, 1.17492847872796, 0, 0, 0.324365845314044, 0, 0.450659762680555, 0, 0, 0, 0, 0.261776863971058, 0.391655320569584, 0.439418377922281, 0, 0, 0, 1.18729238419776, 0, 0, 0.639982443341956, 0, 0, 0, 1.18328702952426, 0, 0, 0, 0, 0, 0.52291366363535, 0, 0, 0.30776128175751, 0.63048112468638, 0.423916619335364, 0.740228437724639, 0, 0.302878458215287, 0.787598752309517, 0, 0, 0, 0, 0, 1.49029992547243, 1.49029992547243, 0, 1.78291660481264, 0.449701188694977, 0.707143954725774, 0, 0, 0.246177888432775, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.373893355224808, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1.8973818612066, 0, 0.339498136703041, 0, 0, 0, 4.12099157484716, 0, 0, 0, 0.341152610061095, 1.07789808257079, 0, 0, 0, 0, 0, 0, 0, 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2.54406804435028, 2.69897000433602, 1.99563519459755, -0.00436480540245009, 2.24303804868629, 2.60205999132796, -0.00436480540245009, -0.00436480540245009, -0.00436480540245009, -0.00436480540245009, 2.301008280397, -0.00436480540245009, -0.00436480540245009, -0.00436480540245009, -0.00436480540245009, -0.00436480540245009, 2.41497334797082, -0.00436480540245009, 2.39619934709574, 1.39794000867204, -0.00436480540245009, 2, 1.84509804001426, 2.81291335664286, 2.17606230512508, -0.00436480540245009, 0, 1.90281846808225, 1.60205999132796, 1.65321251377534, -0.00436480540245009, 2.17609125905568, 0.999565488225982, -0.00436480540245009, 2.09691001300806, -0.00436480540245009, 1.69897000433602, -0.00436480540245009, -0.00436480540245009, -0.00436480540245009, 0, 1.69897000433602, 2, 1.16106838547117, 2.54406804435028, -0.00436480540245009, 2, 2, 1.8750612633917, -0.00436480540245009, 2.19033169817029, 2, -0.00436480540245009, 1.17609125905568, -0.00436480540245009, 1.69897000433602, 1.17609125905568, 1.29003461136252, 1.8750612633917, 1.69897000433602, 1.17580163284828, 2, 2.47710677799565, -0.00436480540245009, -0.00436480540245009, -0.00436480540245009, 1.39794000867204, -0.00436480540245009, 1.69888313675259, 1, 1.47712125471966, 1.47712125471966, 1.17580163284828, -0.00436480540245009, 1.39794000867204, -0.00436480540245009, 1.5910646070265, 1.51785541893003, -0.00436480540245009, 1.60205999132796, -0.00436480540245009, 1.96378782734556, -0.00436480540245009, -0.00436480540245009, 1.81291335664286, -0.00436480540245009, 0, -0.00436480540245009, 1.39794000867204, 1.47712125471966, 1.60195140413352, 1.8750612633917, 0.698970004336019, 1.60205999132796, 1.87500335360004, 1.47639682672533, 1.60205999132796, 1.69897000433602, 0, 1.84509804001426, 1.69888313675259, -0.00436480540245009, 1.69897000433602, 0, -0.00436480540245009, -0.00436480540245009, 1.17609125905568, 1.8750612633917, 1.79239168949825, -0.00436480540245009, -0.00436480540245009, 1.39794000867204, -0.00436480540245009, -0.00436480540245009, 1.30102999566398, -0.00436480540245009, 0, 1.30102999566398, -0.00436480540245009, -0.00436480540245009, -0.00436480540245009, -0.00436480540245009, -0.00436480540245009, 0.999565488225982, 1, 1.8750612633917, -0.00436480540245009, 1.99995656838019, -0.00436480540245009, 2, 1.65321251377534, 1.99563519459755, 2.43933269383026, 0.999565488225982, -0.00436480540245009, 1.29994290002277, 2.54405563575924, -0.00436480540245009, 1.69888313675259, 2.17606230512508, 1.698535492562, -0.00436480540245009, 1.69897000433602, 2.07914505333275), .Names = c("1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "22", "23", "24", "25", "27", "29", "30", "31", "32", "34", "35", "36", "38", "39", "41", "42", "43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", "54", "55", "57", "58", "59", "60", "61", "62", "63", "64", "65", "67", "68", "69", "70", "71", "72", "74", "76", "77", "79", "80", "81", "82", "83", "84", "85", "86", "87", "88", "89", "90", "91", "92", "93", "94", "95", "96", "97", "98", "99", "100", "102", "103", "104", "106", "107", "108", "109", "111", "112", "113", "115", "116", "118", "119", "120", "121", "122", "123", "124", "125", "126", "127", "128", "130", "132", "133", "134", "135", "136", "137", "138", "139", "140", "142", "143", "145", "146", "148", "149", "150", "151", "152", "153", "154", "155", "157", "158", "159", "160", "161", "162", "163", "164", "166", "167", "168", "169", "170", "171", "172", "173", "174", "175", "176", "177", "179", "180", "182", "183", "184", "185", "186", "187", "188", "189", "190", "191", "192", "193", "194", "195", "196", "197", "198", "199", "200", "201", "203", "204", "205", "206", "207", "208", "209", "210", "211", "212", "213", "214", "215", "216", "218", "220", "221", "222", "223", "224", "225", "226", "228", "230", "231", "232", "233", "234", "235", "237", "238", "239", "240", "241", "242", "243", "244", "245", "246", "247", "248", "249", "251", "252", "253", "254", "255", "256", "258", "259", "260", "261", "262", "263", "264", "265", "266", "267", "270", "271", "272", "273", "274", "275", "276", "277", "278", "279", "280", "284", "285", "287", "288", "289", "290", "291", "293", "294", "295", "296", "297", "298", "299", "300", "301", "302", "303", "308", "309", "311", "312", "313", "314", "315", "319", "320", "321", "322", "323", "324", "325", "326", "329", "332", "333", "334", "335", "336", "337", "338", "339", "340", "342", "343", "344", "345", "346", "347", "348", "350", "351", "352", "355", "356", "358", "359", "360", "361", "362", "364", "365", "366", "367", "368", "369", "372", "373", "374", "375", "376", "377", "378", "379", "380", "382", "383", "384", "385", "386", "389", "390", "392", "393", "395", "397", "398", "401", "402", "404", "406", "407", "409", "410", "411", "412", "413", "414", "415", "418", "419", "420", "421", "422", "423", "424", "425", "427", "428", "429", "430", "431", "432", "433", "435", "436", "438", "441", "443", "444", "446", "447", "448", "449", "450", "451", "453", "456", "457", "458", "460", "461", "462", "463", "465", "466", "469", "471", "473", "474", "475", "476", "477", "479", "480", "481", "482", "483", "485", "486", "487", "488", "489", "490", "491", "492", "493", "495", "496", "497", "499", "500", "502", "503", "504", "505", "506", "507", "508", "509", "510", "511", "512", "513", "515", "516", "517", "518", "520", "521", "522", "523", "524", "526", "527", "528", "530", "532", "533", "535", "536", "538", "540", "541", "543", "544", "545", "549", "550", "553", "555", "556", "557", "558", "563", "564", "566", "568", "569", "570", "576", "578", "579", "580", "582", "583", "584", "586", "587", "588", "589", "590", "592", "596", "599", "601", "602", "603", "604", "605", "607", "609", "610", "611", "612", "615", "616", "619", "622", "625", "626", "630", "632", "633", "634", "638", "639", "641", "643", "646", "648", "649", "651", "652", "653", "654", "655", "658", "660", "661", "663", "666", "668", "671", "672", "674", "675", "676", "677", "681", "684", "685", "687", "688", "690", "692", "693", "694", "695", "696", "697", "700", "701", "702", "705", "718", "720", "721", "722", "723", "724", "725", "726", "727", "728", "729", "730", "732", "733", "734", "736", "737", "738", "739", "740", "741", "742", "743", "744", "746", "748", "749", "750", "751", "754", "755", "756", "757", "758", "759", "760", "761", "762", "763", "764", "765", "766", "767", "768", "769", "771", "772", "773", "774", "777", "778", "779", "780", "783", "784", "787", "789", "790", "791", "792", "793", "796", "797", "798", "799", "801", "802", "803", "804", "805", "806", "807", "809", "811", "814", "815", "816", "818", "820", "821", "823", "824", "825", "826", "828", "829", "830", "831", "832", "834", "835")), degree = 2, nprune = 256, keepxy = TRUE)
##
## Out of bag statistics:
##
## RMSE Rsquared
## 0% 0.5227 0.09605
## 2.5% 0.5421 0.36678
## 25% 0.6110 0.53855
## 50% 0.6459 0.57921
## 75% 0.6810 0.61183
## 97.5% 0.9167 0.66937
## 100% 2.6802 0.68459
##
## Model Selection Statistics:
##
## Num Terms Num Variables
## Length:1129 Length:1129
## Class :character Class :character
## Mode :character Mode :character
##
## [1] " calling mypredict_mdl for fit:"## Warning in cbind(numTerms, numVar): number of rows of result is not a
## multiple of vector length (arg 1)
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.bagEarth
## feats
## 1 startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 86.298 41.434
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8287557 0.6379618 0.8127487 0.2124493 1.031477
## max.Adj.R.sq.OOB max.Rsquared.fit
## 1 0.1345758 0.5883867
## label step_major step_minor label_minor bgn end
## 13 fit.models_1_RFE.X 2 11 bagEarth 242.931 362.385
## 14 fit.models_1_All.X 3 0 setup 362.386 NA
## elapsed
## 13 119.454
## 14 NA
## label step_major step_minor label_minor bgn end
## 14 fit.models_1_All.X 3 0 setup 362.386 362.393
## 15 fit.models_1_All.X 3 1 glmnet 362.393 NA
## elapsed
## 14 0.007
## 15 NA
## [1] "fitting model: All.X.glmnet"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dgt2.is9,D.ratio.wrds.stop.n.wrds.n,prdl.descr.my.fctr,D.weight.sum.stem.stop.Ratio,storage.fctr,.rnorm,color.fctr,condition.fctr,cellular.fctr,D.chrs.pnct11.n.log,D.wrds.stop.n.log,D.chrs.pnct13.n.log,D.wrds.n.log,D.chrs.n.log,D.weight.post.stop.sum,D.terms.post.stop.n.log,D.weight.post.stem.sum,D.weight.sum,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.chrs.uppr.n.log,D.ratio.weight.sum.wrds.n,startprice.dcm1.is9,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.0523 on full training set
## Length Class Mode
## a0 100 -none- numeric
## beta 15200 dgCMatrix S4
## df 100 -none- numeric
## dim 2 -none- numeric
## lambda 100 -none- numeric
## dev.ratio 100 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 152 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 1.7812085533
## D.ratio.weight.sum.wrds.n
## -0.1550632656
## color.fctrSpace Gray
## 0.0135281261
## color.fctrUnknown
## -0.0528790375
## condition.fctrFor parts or not working
## -0.2008503978
## condition.fctrManufacturer refurbished
## -0.0626535549
## condition.fctrNew
## 0.0713943665
## condition.fctrNew other (see details)
## 0.0095364893
## condition.fctrSeller refurbished
## -0.0572783508
## prdl.descr.my.fctriPad1#0
## -0.0476025970
## prdl.descr.my.fctriPad1#1
## -0.0100620572
## prdl.descr.my.fctriPad2#0
## -0.0177019740
## prdl.descr.my.fctriPad3#0
## -0.0104318331
## prdl.descr.my.fctriPadAir#0
## 0.0488133307
## prdl.descr.my.fctriPadAir2#0
## 0.2248689932
## prdl.descr.my.fctriPadAir2#1
## 0.0500350313
## startprice.dcm1.is9
## -0.6715758621
## startprice.dcm2.is9
## -0.5652818652
## startprice.dgt1.is9
## 0.8152282579
## startprice.dgt2.is9
## 0.1593824297
## storage.fctr64
## 0.0009215058
## cellular.fctr1:carrier.fctrOther
## 0.5176308144
## cellular.fctr1:carrier.fctrSprint
## -0.2162852140
## prdl.descr.my.fctriPad1#1:.clusterid.fctr2
## -0.0328631366
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2
## 0.0048010518
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr2
## 0.1462443108
## prdl.descr.my.fctriPad1#1:.clusterid.fctr3
## -0.0958480091
## prdl.descr.my.fctriPad3#1:.clusterid.fctr3
## -0.2325642746
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr3
## -0.3504793509
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3
## 0.2800432346
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr5
## 0.2397839773
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 1.792585530
## D.ratio.weight.sum.wrds.n
## -0.162498590
## color.fctrSpace Gray
## 0.011217720
## color.fctrUnknown
## -0.060113460
## condition.fctrFor parts or not working
## -0.208176047
## condition.fctrManufacturer refurbished
## -0.090655902
## condition.fctrNew
## 0.072318940
## condition.fctrNew other (see details)
## 0.021350053
## condition.fctrSeller refurbished
## -0.065606885
## prdl.descr.my.fctriPad1#0
## -0.065702631
## prdl.descr.my.fctriPad1#1
## -0.010166579
## prdl.descr.my.fctriPad2#0
## -0.031678290
## prdl.descr.my.fctriPad3#0
## -0.031379510
## prdl.descr.my.fctriPadAir#0
## 0.054984076
## prdl.descr.my.fctriPadAir2#0
## 0.225219644
## prdl.descr.my.fctriPadAir2#1
## 0.057665343
## startprice.dcm1.is9
## -0.679039455
## startprice.dcm2.is9
## -0.565004482
## startprice.dgt1.is9
## 0.823588191
## startprice.dgt2.is9
## 0.159962508
## storage.fctr16
## -0.001402443
## storage.fctr64
## 0.008555052
## cellular.fctr1:carrier.fctrOther
## 0.571832295
## cellular.fctr1:carrier.fctrSprint
## -0.254996483
## prdl.descr.my.fctriPad1#1:.clusterid.fctr2
## -0.051775617
## prdl.descr.my.fctriPadAir2#1:.clusterid.fctr2
## 0.025801175
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr2
## 0.170679122
## prdl.descr.my.fctriPad1#1:.clusterid.fctr3
## -0.114442465
## prdl.descr.my.fctriPad3#1:.clusterid.fctr3
## -0.265412393
## prdl.descr.my.fctriPadAir#1:.clusterid.fctr3
## -0.392088344
## prdl.descr.my.fctriPadmini2#1:.clusterid.fctr3
## 0.324815981
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr4
## -0.018647667
## prdl.descr.my.fctrUnknown#1:.clusterid.fctr5
## 0.282456794
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 All.X.glmnet
## feats
## 1 startprice.dgt1.is9,startprice.dgt2.is9,D.ratio.wrds.stop.n.wrds.n,prdl.descr.my.fctr,D.weight.sum.stem.stop.Ratio,storage.fctr,.rnorm,color.fctr,condition.fctr,cellular.fctr,D.chrs.pnct11.n.log,D.wrds.stop.n.log,D.chrs.pnct13.n.log,D.wrds.n.log,D.chrs.n.log,D.weight.post.stop.sum,D.terms.post.stop.n.log,D.weight.post.stem.sum,D.weight.sum,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.chrs.uppr.n.log,D.ratio.weight.sum.wrds.n,startprice.dcm1.is9,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.642 0.049
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.6206435 0.6260497 0.4974337 0.1947409 1.042728
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 -0.08534923 0.5591846 0.03946805 0.04565544
## label step_major step_minor label_minor bgn
## 15 fit.models_1_All.X 3 1 glmnet 362.393
## 16 fit.models_1_Best.Interact 4 0 setup 367.225
## end elapsed
## 15 367.225 4.832
## 16 NA NA
## label step_major step_minor label_minor bgn
## 16 fit.models_1_Best.Interact 4 0 setup 367.225
## 17 fit.models_1_RFE.X.Interact 4 1 glmnet 367.255
## end elapsed
## 16 367.254 0.029
## 17 NA NA
## [1] "fitting model: RFE.X.Interact.glmnet"
## [1] " indep_vars: startprice.dcm2.is9,startprice.dcm1.is9,cellular.fctr*startprice.dgt1.is9,cellular.fctr*prdl.descr.my.fctr,cellular.fctr*startprice.dgt2.is9,cellular.fctr*condition.fctr,cellular.fctr*color.fctr,cellular.fctr*D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.55, lambda = 0.0523 on full training set
## Length Class Mode
## a0 88 -none- numeric
## beta 10120 dgCMatrix S4
## df 88 -none- numeric
## dim 2 -none- numeric
## lambda 88 -none- numeric
## dev.ratio 88 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 115 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## 1.782142e+00
## D.ratio.weight.sum.wrds.n
## -1.559873e-01
## color.fctrSpace Gray
## 2.094263e-02
## color.fctrUnknown
## -1.937717e-02
## condition.fctrFor parts or not working
## -1.917958e-01
## condition.fctrManufacturer refurbished
## -5.913617e-02
## condition.fctrNew
## 7.204786e-02
## condition.fctrNew other (see details)
## 3.613247e-05
## condition.fctrSeller refurbished
## -5.860570e-02
## prdl.descr.my.fctriPad1#0
## -5.528824e-02
## prdl.descr.my.fctriPad1#1
## -1.819678e-02
## prdl.descr.my.fctriPad2#0
## -2.133204e-02
## prdl.descr.my.fctriPadAir2#0
## 2.324308e-01
## prdl.descr.my.fctriPadAir2#1
## 8.392688e-02
## prdl.descr.my.fctriPadmini2#1
## 1.092300e-01
## startprice.dgt1.is9
## 7.733636e-01
## startprice.dgt2.is9
## 1.506793e-01
## startprice.dcm1.is9
## -6.624314e-01
## startprice.dcm2.is9
## -5.831957e-01
## cellular.fctr1:D.ratio.weight.sum.wrds.n
## -8.245530e-02
## cellular.fctr1:color.fctrUnknown
## -1.350668e-01
## cellular.fctrUnknown:color.fctrUnknown
## -2.209254e-02
## cellular.fctr1:color.fctrWhite
## 4.474478e-02
## cellular.fctrUnknown:prdl.descr.my.fctriPad3#0
## -7.437731e-01
## cellular.fctr1:prdl.descr.my.fctriPad3#1
## 1.105639e-01
## cellular.fctrUnknown:prdl.descr.my.fctriPad3#1
## -7.271003e-02
## cellular.fctr1:prdl.descr.my.fctriPadAir#0
## 2.413640e-01
## cellular.fctrUnknown:prdl.descr.my.fctriPadmini#0
## -1.158409e-01
## cellular.fctr1:prdl.descr.my.fctriPadmini#1
## -7.693178e-02
## cellular.fctr1:startprice.dgt1.is9
## 1.285022e-01
## cellular.fctrUnknown:startprice.dgt1.is9
## 4.003933e-02
## cellular.fctrUnknown:startprice.dgt2.is9
## 1.928456e-01
## cellular.fctr1:carrier.fctrOther
## 5.962034e-01
## cellular.fctr1:carrier.fctrSprint
## -2.622836e-01
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## 1.791072627
## D.ratio.weight.sum.wrds.n
## -0.162849507
## color.fctrSpace Gray
## 0.022171405
## color.fctrUnknown
## -0.020800176
## condition.fctrFor parts or not working
## -0.196743012
## condition.fctrManufacturer refurbished
## -0.084224101
## condition.fctrNew
## 0.073904746
## condition.fctrNew other (see details)
## 0.008962570
## condition.fctrSeller refurbished
## -0.065690365
## prdl.descr.my.fctriPad1#0
## -0.070450213
## prdl.descr.my.fctriPad1#1
## -0.023695423
## prdl.descr.my.fctriPad2#0
## -0.035236807
## prdl.descr.my.fctriPad3#0
## -0.007704297
## prdl.descr.my.fctriPadAir2#0
## 0.238058771
## prdl.descr.my.fctriPadAir2#1
## 0.104063494
## prdl.descr.my.fctriPadmini2#1
## 0.128550417
## startprice.dgt1.is9
## 0.777851958
## startprice.dgt2.is9
## 0.149901690
## startprice.dcm1.is9
## -0.668293179
## startprice.dcm2.is9
## -0.586190840
## cellular.fctr1:D.ratio.weight.sum.wrds.n
## -0.093539099
## cellular.fctr1:color.fctrUnknown
## -0.148658963
## cellular.fctrUnknown:color.fctrUnknown
## -0.033375597
## cellular.fctr1:color.fctrWhite
## 0.058262068
## cellular.fctrUnknown:prdl.descr.my.fctriPad3#0
## -0.797498362
## cellular.fctr1:prdl.descr.my.fctriPad3#1
## 0.135823968
## cellular.fctrUnknown:prdl.descr.my.fctriPad3#1
## -0.199492890
## cellular.fctr1:prdl.descr.my.fctriPadAir#0
## 0.268847987
## cellular.fctrUnknown:prdl.descr.my.fctriPadmini#0
## -0.161836109
## cellular.fctr1:prdl.descr.my.fctriPadmini#1
## -0.093573894
## cellular.fctr1:startprice.dgt1.is9
## 0.137417435
## cellular.fctrUnknown:startprice.dgt1.is9
## 0.057364193
## cellular.fctrUnknown:startprice.dgt2.is9
## 0.235529110
## cellular.fctr1:carrier.fctrOther
## 0.658513479
## cellular.fctr1:carrier.fctrSprint
## -0.307249992
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.Interact.glmnet
## feats
## 1 startprice.dcm2.is9,startprice.dcm1.is9,cellular.fctr*startprice.dgt1.is9,cellular.fctr*prdl.descr.my.fctr,cellular.fctr*startprice.dgt2.is9,cellular.fctr*condition.fctr,cellular.fctr*color.fctr,cellular.fctr*D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 2.003 0.025
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.6311524 0.6277709 0.5471574 0.188552 1.046727
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 -0.008318265 0.5567986 0.04203132 0.05397503# Check if other preProcess methods improve model performance
mdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indep_vars_vctr <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse=".")
for (prePr in glb_preproc_methods) {
# The operations are applied in this order:
# Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type=glb_model_type, tune.df=glb_tune_models_df,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
train.method=method, train.preProcess=prePr)),
indep_vars=indep_vars_vctr, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
} ## [1] "fitting model: RFE.X.YeoJohnson.rf"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 14 on full training set
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 621 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 621 -none- numeric
## importance 53 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 621 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 53 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.YeoJohnson.rf
## feats
## 1 startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 8.52 1.809
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8968594 0.4794695 0.8872184 0.2368676 1.015447
## max.Adj.R.sq.OOB max.Rsquared.fit
## 1 0.1614087 0.738351
## [1] "fitting model: RFE.X.center.scale.rf"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 14 on full training set## Warning in preProcess.default(thresh = 0.95, k = 5, method = c("center", :
## These variables have zero variances: cellular.fctr1:carrier.fctrNone,
## cellular.fctrUnknown:carrier.fctrNone, cellular.fctr0:carrier.fctrOther,
## cellular.fctrUnknown:carrier.fctrOther, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 621 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 621 -none- numeric
## importance 53 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 621 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 53 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.center.scale.rf
## feats
## 1 startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 8.624 1.779
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.8971826 0.479184 0.8875718 0.2383591 1.014353
## max.Adj.R.sq.OOB max.Rsquared.fit
## 1 0.1630476 0.7386625
## [1] "fitting model: RFE.X.range.rf"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : myfit_mdl: preProcess
## method: range currently does not work for columns
## with no variance: cellular.fctr1:carrier.fctrNone,
## cellular.fctrUnknown:carrier.fctrNone, cellular.fctr0:carrier.fctrOther,
## cellular.fctrUnknown:carrier.fctrOther, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon## [1] "fitting model: RFE.X.pca.rf"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : myfit_mdl: preProcess
## method: range currently does not work for columns
## with no variance: cellular.fctr1:carrier.fctrNone,
## cellular.fctrUnknown:carrier.fctrNone, cellular.fctr0:carrier.fctrOther,
## cellular.fctrUnknown:carrier.fctrOther, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon## [1] "fitting model: RFE.X.ica.rf"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr"## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : myfit_mdl: preProcess
## method: range currently does not work for columns
## with no variance: cellular.fctr1:carrier.fctrNone,
## cellular.fctrUnknown:carrier.fctrNone, cellular.fctr0:carrier.fctrOther,
## cellular.fctrUnknown:carrier.fctrOther, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon## [1] "fitting model: RFE.X.spatialSign.rf"
## [1] " indep_vars: startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr"
## Aggregating results
## Selecting tuning parameters
## Fitting mtry = 27 on full training set## Warning in preProcess.default(thresh = 0.95, k =
## 5, method = "spatialSign", : These variables have
## zero variances: cellular.fctr1:carrier.fctrNone,
## cellular.fctrUnknown:carrier.fctrNone, cellular.fctr0:carrier.fctrOther,
## cellular.fctrUnknown:carrier.fctrOther, cellular.fctr0:carrier.fctrSprint,
## cellular.fctrUnknown:carrier.fctrSprint, cellular.fctr0:carrier.fctrT-
## Mobile, cellular.fctrUnknown:carrier.fctrT-Mobile,
## cellular.fctr0:carrier.fctrUnknown, cellular.fctr0:carrier.fctrVerizon,
## cellular.fctrUnknown:carrier.fctrVerizon
## Length Class Mode
## call 4 -none- call
## type 1 -none- character
## predicted 621 -none- numeric
## mse 500 -none- numeric
## rsq 500 -none- numeric
## oob.times 621 -none- numeric
## importance 53 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 11 -none- list
## coefs 0 -none- NULL
## y 621 -none- numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 53 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 1 -none- logical
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 RFE.X.spatialSign.rf
## feats
## 1 startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 5 17.021 5.372
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.9253822 0.5078949 0.9184073 0.2229654 1.023666
## max.Adj.R.sq.OOB max.Rsquared.fit
## 1 0.1461318 0.7064077 # If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indep_vars_vctr
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
# require(car)
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# # if vif errors out with "there are aliased coefficients in the model"
# alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glb_fitobs_df[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
# all.equal(glb_allobs_df$S.chrs.uppr.n.log, glb_allobs_df$A.chrs.uppr.n.log)
# cor(glb_allobs_df$S.T.herald, glb_allobs_df$S.T.tribun)
# mydsp_obs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glb_allobs_df[, setdiff(names(glb_allobs_df), myfind_chr_cols_df(glb_allobs_df))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indep_vars_vctr <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
# , 1)[, "feats"]
# indep_vars_vctr <- trim(unlist(strsplit(indep_vars_vctr, "[,]")))
# indep_vars_vctr <- setdiff(indep_vars_vctr, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indep_vars_vctr <- c(NULL
# ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
# ,"prdline.my.fctr*biddable"
# #,"prdline.my.fctr*startprice.log"
# #,"prdline.my.fctr*startprice.diff"
# ,"prdline.my.fctr*condition.fctr"
# ,"prdline.my.fctr*D.terms.post.stop.n"
# #,"prdline.my.fctr*D.terms.post.stem.n"
# ,"prdline.my.fctr*cellular.fctr"
# # ,"<feat1>:<feat2>"
# )
# for (method in glb_mdl_methods) {
# ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glb_tune_models_df)
# csm_mdl_id <- paste0(mdl_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
# method)]]); print(head(csm_featsimp_df))
# }
###
# Ntv.1.lm <- lm(reformulate(indep_vars_vctr, glb_rsp_var), glb_trnobs_df); print(summary(Ntv.1.lm))
#csm_featsimp_df[grepl("H.npnct19.log", row.names(csm_featsimp_df)), , FALSE]
#print(sprintf("%s OOB confusion matrix & accuracy: ", csm_mdl_id)); print(t(confusionMatrix(csm_OOBobs_df[, paste0(glb_rsp_var_out, csm_mdl_id)], csm_OOBobs_df[, glb_rsp_var])$table))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$importance)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$importance)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
# User specified bivariate models
# indep_vars_vctr_lst <- list()
# for (feat in setdiff(names(glb_fitobs_df),
# union(glb_rsp_var, glb_exclude_vars_as_features)))
# indep_vars_vctr_lst[["feat"]] <- feat
# User specified combinatorial models
# indep_vars_vctr_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indep_vars_vctr_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
# indep_vars_vctr=indep_vars_vctr,
# rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
# fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glb_tune_models_df,
# model_loss_mtrx=glb_model_metric_terms,
# model_summaryFunction=glb_model_metric_smmry,
# model_metric=glb_model_metric,
# model_metric_maximize=glb_model_metric_maximize)
# Simplify a model
# fit_df <- glb_fitobs_df; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glb_fitobs_df, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glb_model_metric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)## id
## MFO.lm MFO.lm
## Max.cor.Y.rcv.1X1.glmnet Max.cor.Y.rcv.1X1.glmnet
## Max.cor.Y.rcv.3X1.glmnet Max.cor.Y.rcv.3X1.glmnet
## Max.cor.Y.rcv.3X3.glmnet Max.cor.Y.rcv.3X3.glmnet
## Max.cor.Y.rcv.3X5.glmnet Max.cor.Y.rcv.3X5.glmnet
## Max.cor.Y.rcv.5X1.glmnet Max.cor.Y.rcv.5X1.glmnet
## Max.cor.Y.rcv.5X3.glmnet Max.cor.Y.rcv.5X3.glmnet
## Max.cor.Y.rcv.5X5.glmnet Max.cor.Y.rcv.5X5.glmnet
## Max.cor.Y.rcv.1X1.cp.0.rpart Max.cor.Y.rcv.1X1.cp.0.rpart
## Max.cor.Y.rpart Max.cor.Y.rpart
## Interact.High.cor.Y.glmnet Interact.High.cor.Y.glmnet
## Low.cor.X.glmnet Low.cor.X.glmnet
## RFE.X.glm RFE.X.glm
## RFE.X.glmnet RFE.X.glmnet
## RFE.X.rpart RFE.X.rpart
## RFE.X.gbm RFE.X.gbm
## RFE.X.rf RFE.X.rf
## RFE.X.nnet RFE.X.nnet
## RFE.X.avNNet RFE.X.avNNet
## RFE.X.svmLinear RFE.X.svmLinear
## RFE.X.svmLinear2 RFE.X.svmLinear2
## RFE.X.earth RFE.X.earth
## RFE.X.bagEarth RFE.X.bagEarth
## All.X.glmnet All.X.glmnet
## RFE.X.Interact.glmnet RFE.X.Interact.glmnet
## RFE.X.YeoJohnson.rf RFE.X.YeoJohnson.rf
## RFE.X.center.scale.rf RFE.X.center.scale.rf
## RFE.X.spatialSign.rf RFE.X.spatialSign.rf
## feats
## MFO.lm .rnorm
## Max.cor.Y.rcv.1X1.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## Max.cor.Y.rcv.3X1.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## Max.cor.Y.rcv.3X3.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## Max.cor.Y.rcv.3X5.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## Max.cor.Y.rcv.5X1.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## Max.cor.Y.rcv.5X3.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## Max.cor.Y.rcv.5X5.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## Max.cor.Y.rcv.1X1.cp.0.rpart startprice.dcm2.is9,startprice.dgt1.is9
## Max.cor.Y.rpart startprice.dcm2.is9,startprice.dgt1.is9
## Interact.High.cor.Y.glmnet startprice.dcm2.is9,startprice.dgt1.is9,startprice.dcm2.is9:cellular.fctr,startprice.dcm2.is9:D.chrs.pnct13.n.log,startprice.dcm2.is9:D.chrs.uppr.n.log,startprice.dcm2.is9:D.weight.post.stem.sum,startprice.dcm2.is9:D.terms.post.stem.n.log,startprice.dcm2.is9:D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9:startprice.dcm2.is9
## Low.cor.X.glmnet startprice.dgt1.is9,startprice.dgt2.is9,D.ratio.wrds.stop.n.wrds.n,prdl.descr.my.fctr,D.weight.sum.stem.stop.Ratio,storage.fctr,.rnorm,color.fctr,condition.fctr,cellular.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,D.ratio.weight.sum.wrds.n,startprice.dcm2.is9,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.glm startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.glmnet startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.rpart startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.gbm startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.rf startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.nnet startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.avNNet startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.svmLinear startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.svmLinear2 startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.earth startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.bagEarth startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## All.X.glmnet startprice.dgt1.is9,startprice.dgt2.is9,D.ratio.wrds.stop.n.wrds.n,prdl.descr.my.fctr,D.weight.sum.stem.stop.Ratio,storage.fctr,.rnorm,color.fctr,condition.fctr,cellular.fctr,D.chrs.pnct11.n.log,D.wrds.stop.n.log,D.chrs.pnct13.n.log,D.wrds.n.log,D.chrs.n.log,D.weight.post.stop.sum,D.terms.post.stop.n.log,D.weight.post.stem.sum,D.weight.sum,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.chrs.uppr.n.log,D.ratio.weight.sum.wrds.n,startprice.dcm1.is9,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.Interact.glmnet startprice.dcm2.is9,startprice.dcm1.is9,cellular.fctr*startprice.dgt1.is9,cellular.fctr*prdl.descr.my.fctr,cellular.fctr*startprice.dgt2.is9,cellular.fctr*condition.fctr,cellular.fctr*color.fctr,cellular.fctr*D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr
## RFE.X.YeoJohnson.rf startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.center.scale.rf startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.spatialSign.rf startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns min.elapsedtime.everything
## MFO.lm 0 0.538
## Max.cor.Y.rcv.1X1.glmnet 0 0.850
## Max.cor.Y.rcv.3X1.glmnet 25 1.235
## Max.cor.Y.rcv.3X3.glmnet 25 4.184
## Max.cor.Y.rcv.3X5.glmnet 25 2.167
## Max.cor.Y.rcv.5X1.glmnet 25 1.480
## Max.cor.Y.rcv.5X3.glmnet 25 2.056
## Max.cor.Y.rcv.5X5.glmnet 25 2.377
## Max.cor.Y.rcv.1X1.cp.0.rpart 0 0.697
## Max.cor.Y.rpart 5 1.177
## Interact.High.cor.Y.glmnet 25 1.755
## Low.cor.X.glmnet 25 1.846
## RFE.X.glm 1 1.198
## RFE.X.glmnet 25 1.715
## RFE.X.rpart 5 1.500
## RFE.X.gbm 1 2.940
## RFE.X.rf 5 9.204
## RFE.X.nnet 25 13.548
## RFE.X.avNNet 25 23.452
## RFE.X.svmLinear 5 2.620
## RFE.X.svmLinear2 5 2.160
## RFE.X.earth 5 3.190
## RFE.X.bagEarth 1 86.298
## All.X.glmnet 25 2.642
## RFE.X.Interact.glmnet 25 2.003
## RFE.X.YeoJohnson.rf 5 8.520
## RFE.X.center.scale.rf 5 8.624
## RFE.X.spatialSign.rf 5 17.021
## min.elapsedtime.final max.R.sq.fit
## MFO.lm 0.004 0.001918896
## Max.cor.Y.rcv.1X1.glmnet 0.010 0.523100930
## Max.cor.Y.rcv.3X1.glmnet 0.005 0.523203036
## Max.cor.Y.rcv.3X3.glmnet 0.007 0.523205687
## Max.cor.Y.rcv.3X5.glmnet 0.005 0.523205687
## Max.cor.Y.rcv.5X1.glmnet 0.005 0.523204687
## Max.cor.Y.rcv.5X3.glmnet 0.004 0.523204687
## Max.cor.Y.rcv.5X5.glmnet 0.006 0.523204687
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.010 0.662176564
## Max.cor.Y.rpart 0.009 0.661469678
## Interact.High.cor.Y.glmnet 0.006 0.523656197
## Low.cor.X.glmnet 0.013 0.576187931
## RFE.X.glm 0.028 0.645825219
## RFE.X.glmnet 0.008 0.628758934
## RFE.X.rpart 0.037 0.738383665
## RFE.X.gbm 0.693 0.789031593
## RFE.X.rf 1.755 0.897182567
## RFE.X.nnet 0.336 0.359982951
## RFE.X.avNNet 0.576 0.359251285
## RFE.X.svmLinear 0.065 0.575965941
## RFE.X.svmLinear2 0.084 0.577201172
## RFE.X.earth 0.201 0.620704234
## RFE.X.bagEarth 41.434 0.828755678
## All.X.glmnet 0.049 0.620643509
## RFE.X.Interact.glmnet 0.025 0.631152416
## RFE.X.YeoJohnson.rf 1.809 0.896859364
## RFE.X.center.scale.rf 1.779 0.897182567
## RFE.X.spatialSign.rf 5.372 0.925382190
## min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB
## MFO.lm 0.9364496 0.0003064874 1.783336e-05
## Max.cor.Y.rcv.1X1.glmnet 0.6473136 0.5215575671 1.530671e-01
## Max.cor.Y.rcv.3X1.glmnet 0.6483723 0.5216600038 1.519868e-01
## Max.cor.Y.rcv.3X3.glmnet 0.6488737 0.5216626630 1.520071e-01
## Max.cor.Y.rcv.3X5.glmnet 0.6491051 0.5216626630 1.520071e-01
## Max.cor.Y.rcv.5X1.glmnet 0.6486277 0.5216616597 1.521632e-01
## Max.cor.Y.rcv.5X3.glmnet 0.6479740 0.5216616597 1.521632e-01
## Max.cor.Y.rcv.5X5.glmnet 0.6474622 0.5216616597 1.521632e-01
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.5448116 NA 2.295076e-01
## Max.cor.Y.rpart 0.5425538 NA 2.237461e-01
## Interact.High.cor.Y.glmnet 0.6508063 0.5166396765 1.555183e-01
## Low.cor.X.glmnet 0.6377835 0.4713008397 2.003791e-01
## RFE.X.glm 0.6340075 0.6213993720 1.657697e-01
## RFE.X.glmnet 0.6164891 0.5940573881 1.976359e-01
## RFE.X.rpart 0.5186586 NA 2.105739e-01
## RFE.X.gbm 0.5057258 0.7693114426 2.407367e-01
## RFE.X.rf 0.4813558 0.8875717665 2.383591e-01
## RFE.X.nnet 0.9287422 0.3001577246 1.818120e-01
## RFE.X.avNNet 0.9285701 0.3487472073 1.801205e-01
## RFE.X.svmLinear 0.6324697 0.5690145632 1.240590e-01
## RFE.X.svmLinear2 0.6335311 0.5702700433 1.240085e-01
## RFE.X.earth 0.6332435 0.5852497794 1.618643e-01
## RFE.X.bagEarth 0.6379618 0.8127487129 2.124493e-01
## All.X.glmnet 0.6260497 0.4974337085 1.947409e-01
## RFE.X.Interact.glmnet 0.6277709 0.5471574216 1.885520e-01
## RFE.X.YeoJohnson.rf 0.4794695 0.8872183523 2.368676e-01
## RFE.X.center.scale.rf 0.4791840 0.8875717665 2.383591e-01
## RFE.X.spatialSign.rf 0.5078949 0.9184073331 2.229654e-01
## min.RMSE.OOB max.Adj.R.sq.OOB
## MFO.lm 1.161981 -0.001682817
## Max.cor.Y.rcv.1X1.glmnet 1.069369 0.150181432
## Max.cor.Y.rcv.3X1.glmnet 1.070051 0.149097479
## Max.cor.Y.rcv.3X3.glmnet 1.070038 0.149117854
## Max.cor.Y.rcv.3X5.glmnet 1.070038 0.149117854
## Max.cor.Y.rcv.5X1.glmnet 1.069939 0.149274527
## Max.cor.Y.rcv.5X3.glmnet 1.069939 0.149274527
## Max.cor.Y.rcv.5X5.glmnet 1.069939 0.149274527
## Max.cor.Y.rcv.1X1.cp.0.rpart 1.019970 NA
## Max.cor.Y.rpart 1.023776 NA
## Interact.High.cor.Y.glmnet 1.067820 0.142414265
## Low.cor.X.glmnet 1.039071 -0.010679695
## RFE.X.glm 1.061319 0.104987850
## RFE.X.glmnet 1.040852 0.118297665
## RFE.X.rpart 1.032426 NA
## RFE.X.gbm 1.012759 0.165660344
## RFE.X.rf 1.014353 0.163047623
## RFE.X.nnet 1.312990 0.100909083
## RFE.X.avNNet 1.312557 0.165960259
## RFE.X.svmLinear 1.085650 0.108930484
## RFE.X.svmLinear2 1.084196 0.108879078
## RFE.X.earth 1.063801 0.078988994
## RFE.X.bagEarth 1.031477 0.134575767
## All.X.glmnet 1.042728 -0.085349229
## RFE.X.Interact.glmnet 1.046727 -0.008318265
## RFE.X.YeoJohnson.rf 1.015447 0.161408655
## RFE.X.center.scale.rf 1.014353 0.163047623
## RFE.X.spatialSign.rf 1.023666 0.146131795
## max.Rsquared.fit min.RMSESD.fit
## MFO.lm NA NA
## Max.cor.Y.rcv.1X1.glmnet NA NA
## Max.cor.Y.rcv.3X1.glmnet 0.5230352 0.01939618
## Max.cor.Y.rcv.3X3.glmnet 0.5245475 0.04964437
## Max.cor.Y.rcv.3X5.glmnet 0.5249564 0.05053156
## Max.cor.Y.rcv.5X1.glmnet 0.5273959 0.05853097
## Max.cor.Y.rcv.5X3.glmnet 0.5269525 0.05541664
## Max.cor.Y.rcv.5X5.glmnet 0.5265158 0.05386228
## Max.cor.Y.rcv.1X1.cp.0.rpart NA NA
## Max.cor.Y.rpart 0.6647272 0.08229901
## Interact.High.cor.Y.glmnet 0.5217293 0.04930768
## Low.cor.X.glmnet 0.5447478 0.03682266
## RFE.X.glm 0.5550280 0.04132701
## RFE.X.glmnet 0.5730742 0.04123952
## RFE.X.rpart 0.6935940 0.06786912
## RFE.X.gbm 0.7075303 0.07012577
## RFE.X.rf 0.7362883 NA
## RFE.X.nnet 0.6532849 0.02330897
## RFE.X.avNNet 0.6527267 0.02312541
## RFE.X.svmLinear 0.5621811 0.05924106
## RFE.X.svmLinear2 0.5656896 0.05951189
## RFE.X.earth 0.5522804 0.05384071
## RFE.X.bagEarth 0.5883867 NA
## All.X.glmnet 0.5591846 0.03946805
## RFE.X.Interact.glmnet 0.5567986 0.04203132
## RFE.X.YeoJohnson.rf 0.7383510 NA
## RFE.X.center.scale.rf 0.7386625 NA
## RFE.X.spatialSign.rf 0.7064077 NA
## max.RsquaredSD.fit min.aic.fit
## MFO.lm NA NA
## Max.cor.Y.rcv.1X1.glmnet NA NA
## Max.cor.Y.rcv.3X1.glmnet 0.01019279 NA
## Max.cor.Y.rcv.3X3.glmnet 0.05637477 NA
## Max.cor.Y.rcv.3X5.glmnet 0.05992791 NA
## Max.cor.Y.rcv.5X1.glmnet 0.07367248 NA
## Max.cor.Y.rcv.5X3.glmnet 0.06285653 NA
## Max.cor.Y.rcv.5X5.glmnet 0.05883978 NA
## Max.cor.Y.rcv.1X1.cp.0.rpart NA NA
## Max.cor.Y.rpart 0.09004506 NA
## Interact.High.cor.Y.glmnet 0.05705445 NA
## Low.cor.X.glmnet 0.04391066 NA
## RFE.X.glm 0.04178663 1121.389
## RFE.X.glmnet 0.04770118 NA
## RFE.X.rpart 0.06969270 NA
## RFE.X.gbm 0.07101878 NA
## RFE.X.rf NA NA
## RFE.X.nnet 0.07391302 NA
## RFE.X.avNNet 0.07095136 NA
## RFE.X.svmLinear 0.06419741 NA
## RFE.X.svmLinear2 0.06281370 NA
## RFE.X.earth 0.06014198 NA
## RFE.X.bagEarth NA NA
## All.X.glmnet 0.04565544 NA
## RFE.X.Interact.glmnet 0.05397503 NA
## RFE.X.YeoJohnson.rf NA NA
## RFE.X.center.scale.rf NA NA
## RFE.X.spatialSign.rf NA NArm(ret_lst)
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end",
major.inc=TRUE, label.minor="teardown")## label step_major step_minor label_minor bgn
## 17 fit.models_1_RFE.X.Interact 4 1 glmnet 367.255
## 18 fit.models_1_end 5 0 teardown 414.721
## end elapsed
## 17 414.72 47.465
## 18 NA NAglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 11 fit.models 7 1 1 156.490 414.728 258.238
## 12 fit.models 7 2 2 414.728 NA NAif (!is.null(glb_model_metric_smmry)) {
stats_df <- glb_models_df[, "id", FALSE]
stats_mdl_df <- data.frame()
for (mdl_id in stats_df$id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[mdl_id]], glb_fitobs_df, glb_rsp_var,
glb_rsp_var_out, mdl_id, "fit",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
stats_mdl_df <- data.frame()
for (mdl_id in stats_df$id) {
stats_mdl_df <- rbind(stats_mdl_df,
mypredict_mdl(glb_models_lst[[mdl_id]], glb_OOBobs_df, glb_rsp_var,
glb_rsp_var_out, mdl_id, "OOB",
glb_model_metric_smmry, glb_model_metric,
glb_model_metric_maximize, ret_type="stats"))
}
stats_df <- merge(stats_df, stats_mdl_df, all.x=TRUE)
print("Merging following data into glb_models_df:")
print(stats_mrg_df <- stats_df[, c(1, grep(glb_model_metric, names(stats_df)))])
print(tmp_models_df <- orderBy(~mdl_id, glb_models_df[, c("id",
grep(glb_model_metric, names(stats_df), value=TRUE))]))
tmp2_models_df <- glb_models_df[, c("id", setdiff(names(glb_models_df),
grep(glb_model_metric, names(stats_df), value=TRUE)))]
tmp3_models_df <- merge(tmp2_models_df, stats_mrg_df, all.x=TRUE, sort=FALSE)
print(tmp3_models_df)
print(names(tmp3_models_df))
print(glb_models_df <- subset(tmp3_models_df, select=-mdl_id.1))
}
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)## id
## MFO.lm MFO.lm
## Max.cor.Y.rcv.1X1.glmnet Max.cor.Y.rcv.1X1.glmnet
## Max.cor.Y.rcv.3X1.glmnet Max.cor.Y.rcv.3X1.glmnet
## Max.cor.Y.rcv.3X3.glmnet Max.cor.Y.rcv.3X3.glmnet
## Max.cor.Y.rcv.3X5.glmnet Max.cor.Y.rcv.3X5.glmnet
## Max.cor.Y.rcv.5X1.glmnet Max.cor.Y.rcv.5X1.glmnet
## Max.cor.Y.rcv.5X3.glmnet Max.cor.Y.rcv.5X3.glmnet
## Max.cor.Y.rcv.5X5.glmnet Max.cor.Y.rcv.5X5.glmnet
## Max.cor.Y.rcv.1X1.cp.0.rpart Max.cor.Y.rcv.1X1.cp.0.rpart
## Max.cor.Y.rpart Max.cor.Y.rpart
## Interact.High.cor.Y.glmnet Interact.High.cor.Y.glmnet
## Low.cor.X.glmnet Low.cor.X.glmnet
## RFE.X.glm RFE.X.glm
## RFE.X.glmnet RFE.X.glmnet
## RFE.X.rpart RFE.X.rpart
## RFE.X.gbm RFE.X.gbm
## RFE.X.rf RFE.X.rf
## RFE.X.nnet RFE.X.nnet
## RFE.X.avNNet RFE.X.avNNet
## RFE.X.svmLinear RFE.X.svmLinear
## RFE.X.svmLinear2 RFE.X.svmLinear2
## RFE.X.earth RFE.X.earth
## RFE.X.bagEarth RFE.X.bagEarth
## All.X.glmnet All.X.glmnet
## RFE.X.Interact.glmnet RFE.X.Interact.glmnet
## RFE.X.YeoJohnson.rf RFE.X.YeoJohnson.rf
## RFE.X.center.scale.rf RFE.X.center.scale.rf
## RFE.X.spatialSign.rf RFE.X.spatialSign.rf
## feats
## MFO.lm .rnorm
## Max.cor.Y.rcv.1X1.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## Max.cor.Y.rcv.3X1.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## Max.cor.Y.rcv.3X3.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## Max.cor.Y.rcv.3X5.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## Max.cor.Y.rcv.5X1.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## Max.cor.Y.rcv.5X3.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## Max.cor.Y.rcv.5X5.glmnet startprice.dcm2.is9,startprice.dgt1.is9
## Max.cor.Y.rcv.1X1.cp.0.rpart startprice.dcm2.is9,startprice.dgt1.is9
## Max.cor.Y.rpart startprice.dcm2.is9,startprice.dgt1.is9
## Interact.High.cor.Y.glmnet startprice.dcm2.is9,startprice.dgt1.is9,startprice.dcm2.is9:cellular.fctr,startprice.dcm2.is9:D.chrs.pnct13.n.log,startprice.dcm2.is9:D.chrs.uppr.n.log,startprice.dcm2.is9:D.weight.post.stem.sum,startprice.dcm2.is9:D.terms.post.stem.n.log,startprice.dcm2.is9:D.ratio.wrds.stop.n.wrds.n,startprice.dcm2.is9:startprice.dcm2.is9
## Low.cor.X.glmnet startprice.dgt1.is9,startprice.dgt2.is9,D.ratio.wrds.stop.n.wrds.n,prdl.descr.my.fctr,D.weight.sum.stem.stop.Ratio,storage.fctr,.rnorm,color.fctr,condition.fctr,cellular.fctr,D.chrs.pnct11.n.log,D.chrs.pnct13.n.log,D.ratio.weight.sum.wrds.n,startprice.dcm2.is9,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.glm startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.glmnet startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.rpart startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.gbm startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.rf startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.nnet startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.avNNet startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.svmLinear startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.svmLinear2 startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.earth startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.bagEarth startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## All.X.glmnet startprice.dgt1.is9,startprice.dgt2.is9,D.ratio.wrds.stop.n.wrds.n,prdl.descr.my.fctr,D.weight.sum.stem.stop.Ratio,storage.fctr,.rnorm,color.fctr,condition.fctr,cellular.fctr,D.chrs.pnct11.n.log,D.wrds.stop.n.log,D.chrs.pnct13.n.log,D.wrds.n.log,D.chrs.n.log,D.weight.post.stop.sum,D.terms.post.stop.n.log,D.weight.post.stem.sum,D.weight.sum,D.terms.post.stem.n.log,D.wrds.unq.n.log,D.chrs.uppr.n.log,D.ratio.weight.sum.wrds.n,startprice.dcm1.is9,startprice.dcm2.is9,cellular.fctr:carrier.fctr,prdl.descr.my.fctr:.clusterid.fctr
## RFE.X.Interact.glmnet startprice.dcm2.is9,startprice.dcm1.is9,cellular.fctr*startprice.dgt1.is9,cellular.fctr*prdl.descr.my.fctr,cellular.fctr*startprice.dgt2.is9,cellular.fctr*condition.fctr,cellular.fctr*color.fctr,cellular.fctr*D.ratio.weight.sum.wrds.n,cellular.fctr:carrier.fctr
## RFE.X.YeoJohnson.rf startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.center.scale.rf startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## RFE.X.spatialSign.rf startprice.dgt1.is9,startprice.dcm2.is9,startprice.dcm1.is9,startprice.dgt2.is9,condition.fctr,prdl.descr.my.fctr,color.fctr,D.ratio.weight.sum.wrds.n,cellular.fctr,cellular.fctr:carrier.fctr
## max.nTuningRuns max.R.sq.fit max.Adj.R.sq.fit
## MFO.lm 0 0.001918896 0.0003064874
## Max.cor.Y.rcv.1X1.glmnet 0 0.523100930 0.5215575671
## Max.cor.Y.rcv.3X1.glmnet 25 0.523203036 0.5216600038
## Max.cor.Y.rcv.3X3.glmnet 25 0.523205687 0.5216626630
## Max.cor.Y.rcv.3X5.glmnet 25 0.523205687 0.5216626630
## Max.cor.Y.rcv.5X1.glmnet 25 0.523204687 0.5216616597
## Max.cor.Y.rcv.5X3.glmnet 25 0.523204687 0.5216616597
## Max.cor.Y.rcv.5X5.glmnet 25 0.523204687 0.5216616597
## Max.cor.Y.rcv.1X1.cp.0.rpart 0 0.662176564 NA
## Max.cor.Y.rpart 5 0.661469678 NA
## Interact.High.cor.Y.glmnet 25 0.523656197 0.5166396765
## Low.cor.X.glmnet 25 0.576187931 0.4713008397
## RFE.X.glm 1 0.645825219 0.6213993720
## RFE.X.glmnet 25 0.628758934 0.5940573881
## RFE.X.rpart 5 0.738383665 NA
## RFE.X.gbm 1 0.789031593 0.7693114426
## RFE.X.rf 5 0.897182567 0.8875717665
## RFE.X.nnet 25 0.359982951 0.3001577246
## RFE.X.avNNet 25 0.359251285 0.3487472073
## RFE.X.svmLinear 5 0.575965941 0.5690145632
## RFE.X.svmLinear2 5 0.577201172 0.5702700433
## RFE.X.earth 5 0.620704234 0.5852497794
## RFE.X.bagEarth 1 0.828755678 0.8127487129
## All.X.glmnet 25 0.620643509 0.4974337085
## RFE.X.Interact.glmnet 25 0.631152416 0.5471574216
## RFE.X.YeoJohnson.rf 5 0.896859364 0.8872183523
## RFE.X.center.scale.rf 5 0.897182567 0.8875717665
## RFE.X.spatialSign.rf 5 0.925382190 0.9184073331
## max.R.sq.OOB max.Adj.R.sq.OOB
## MFO.lm 1.783336e-05 -0.001682817
## Max.cor.Y.rcv.1X1.glmnet 1.530671e-01 0.150181432
## Max.cor.Y.rcv.3X1.glmnet 1.519868e-01 0.149097479
## Max.cor.Y.rcv.3X3.glmnet 1.520071e-01 0.149117854
## Max.cor.Y.rcv.3X5.glmnet 1.520071e-01 0.149117854
## Max.cor.Y.rcv.5X1.glmnet 1.521632e-01 0.149274527
## Max.cor.Y.rcv.5X3.glmnet 1.521632e-01 0.149274527
## Max.cor.Y.rcv.5X5.glmnet 1.521632e-01 0.149274527
## Max.cor.Y.rcv.1X1.cp.0.rpart 2.295076e-01 NA
## Max.cor.Y.rpart 2.237461e-01 NA
## Interact.High.cor.Y.glmnet 1.555183e-01 0.142414265
## Low.cor.X.glmnet 2.003791e-01 -0.010679695
## RFE.X.glm 1.657697e-01 0.104987850
## RFE.X.glmnet 1.976359e-01 0.118297665
## RFE.X.rpart 2.105739e-01 NA
## RFE.X.gbm 2.407367e-01 0.165660344
## RFE.X.rf 2.383591e-01 0.163047623
## RFE.X.nnet 1.818120e-01 0.100909083
## RFE.X.avNNet 1.801205e-01 0.165960259
## RFE.X.svmLinear 1.240590e-01 0.108930484
## RFE.X.svmLinear2 1.240085e-01 0.108879078
## RFE.X.earth 1.618643e-01 0.078988994
## RFE.X.bagEarth 2.124493e-01 0.134575767
## All.X.glmnet 1.947409e-01 -0.085349229
## RFE.X.Interact.glmnet 1.885520e-01 -0.008318265
## RFE.X.YeoJohnson.rf 2.368676e-01 0.161408655
## RFE.X.center.scale.rf 2.383591e-01 0.163047623
## RFE.X.spatialSign.rf 2.229654e-01 0.146131795
## max.Rsquared.fit inv.elapsedtime.everything
## MFO.lm NA 1.85873606
## Max.cor.Y.rcv.1X1.glmnet NA 1.17647059
## Max.cor.Y.rcv.3X1.glmnet 0.5230352 0.80971660
## Max.cor.Y.rcv.3X3.glmnet 0.5245475 0.23900574
## Max.cor.Y.rcv.3X5.glmnet 0.5249564 0.46146747
## Max.cor.Y.rcv.5X1.glmnet 0.5273959 0.67567568
## Max.cor.Y.rcv.5X3.glmnet 0.5269525 0.48638132
## Max.cor.Y.rcv.5X5.glmnet 0.5265158 0.42069836
## Max.cor.Y.rcv.1X1.cp.0.rpart NA 1.43472023
## Max.cor.Y.rpart 0.6647272 0.84961767
## Interact.High.cor.Y.glmnet 0.5217293 0.56980057
## Low.cor.X.glmnet 0.5447478 0.54171181
## RFE.X.glm 0.5550280 0.83472454
## RFE.X.glmnet 0.5730742 0.58309038
## RFE.X.rpart 0.6935940 0.66666667
## RFE.X.gbm 0.7075303 0.34013605
## RFE.X.rf 0.7362883 0.10864841
## RFE.X.nnet 0.6532849 0.07381163
## RFE.X.avNNet 0.6527267 0.04264029
## RFE.X.svmLinear 0.5621811 0.38167939
## RFE.X.svmLinear2 0.5656896 0.46296296
## RFE.X.earth 0.5522804 0.31347962
## RFE.X.bagEarth 0.5883867 0.01158775
## All.X.glmnet 0.5591846 0.37850114
## RFE.X.Interact.glmnet 0.5567986 0.49925112
## RFE.X.YeoJohnson.rf 0.7383510 0.11737089
## RFE.X.center.scale.rf 0.7386625 0.11595547
## RFE.X.spatialSign.rf 0.7064077 0.05875095
## inv.elapsedtime.final inv.RMSE.fit
## MFO.lm 250.00000000 1.067863
## Max.cor.Y.rcv.1X1.glmnet 100.00000000 1.544846
## Max.cor.Y.rcv.3X1.glmnet 200.00000000 1.542324
## Max.cor.Y.rcv.3X3.glmnet 142.85714286 1.541132
## Max.cor.Y.rcv.3X5.glmnet 200.00000000 1.540582
## Max.cor.Y.rcv.5X1.glmnet 200.00000000 1.541717
## Max.cor.Y.rcv.5X3.glmnet 250.00000000 1.543272
## Max.cor.Y.rcv.5X5.glmnet 166.66666667 1.544492
## Max.cor.Y.rcv.1X1.cp.0.rpart 100.00000000 1.835497
## Max.cor.Y.rpart 111.11111111 1.843135
## Interact.High.cor.Y.glmnet 166.66666667 1.536555
## Low.cor.X.glmnet 76.92307692 1.567930
## RFE.X.glm 35.71428571 1.577268
## RFE.X.glmnet 125.00000000 1.622089
## RFE.X.rpart 27.02702703 1.928051
## RFE.X.gbm 1.44300144 1.977356
## RFE.X.rf 0.56980057 2.077465
## RFE.X.nnet 2.97619048 1.076725
## RFE.X.avNNet 1.73611111 1.076925
## RFE.X.svmLinear 15.38461538 1.581103
## RFE.X.svmLinear2 11.90476190 1.578454
## RFE.X.earth 4.97512438 1.579171
## RFE.X.bagEarth 0.02413477 1.567492
## All.X.glmnet 20.40816327 1.597317
## RFE.X.Interact.glmnet 40.00000000 1.592938
## RFE.X.YeoJohnson.rf 0.55279160 2.085638
## RFE.X.center.scale.rf 0.56211355 2.086881
## RFE.X.spatialSign.rf 0.18615041 1.968911
## inv.RMSE.OOB inv.aic.fit
## MFO.lm 0.8605989 NA
## Max.cor.Y.rcv.1X1.glmnet 0.9351309 NA
## Max.cor.Y.rcv.3X1.glmnet 0.9345351 NA
## Max.cor.Y.rcv.3X3.glmnet 0.9345463 NA
## Max.cor.Y.rcv.3X5.glmnet 0.9345463 NA
## Max.cor.Y.rcv.5X1.glmnet 0.9346323 NA
## Max.cor.Y.rcv.5X3.glmnet 0.9346323 NA
## Max.cor.Y.rcv.5X5.glmnet 0.9346323 NA
## Max.cor.Y.rcv.1X1.cp.0.rpart 0.9804213 NA
## Max.cor.Y.rpart 0.9767761 NA
## Interact.High.cor.Y.glmnet 0.9364871 NA
## Low.cor.X.glmnet 0.9623983 NA
## RFE.X.glm 0.9422235 0.0008917511
## RFE.X.glmnet 0.9607517 NA
## RFE.X.rpart 0.9685927 NA
## RFE.X.gbm 0.9874018 NA
## RFE.X.rf 0.9858505 NA
## RFE.X.nnet 0.7616205 NA
## RFE.X.avNNet 0.7618719 NA
## RFE.X.svmLinear 0.9211076 NA
## RFE.X.svmLinear2 0.9223423 NA
## RFE.X.earth 0.9400258 NA
## RFE.X.bagEarth 0.9694834 NA
## All.X.glmnet 0.9590232 NA
## RFE.X.Interact.glmnet 0.9553590 NA
## RFE.X.YeoJohnson.rf 0.9847878 NA
## RFE.X.center.scale.rf 0.9858505 NA
## RFE.X.spatialSign.rf 0.9768808 NAprint(myplot_radar(radar_inp_df=plt_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 28. Consider specifying shapes manually if you must have them.## Warning: Removed 36 rows containing missing values (geom_path).## Warning: Removed 254 rows containing missing values (geom_point).## Warning: Removed 36 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 28. Consider specifying shapes manually if you must have them.
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}## [1] "var:min.RMSESD.fit"
## [1] "var:max.RsquaredSD.fit"# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")],
# all.x=TRUE)
png(paste0(glb_out_pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") +
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))## Warning: Removed 3 rows containing missing values (position_stack).## Warning: Removed 20 rows containing missing values (geom_errorbar).dev.off()## quartz_off_screen
## 2print(gp)## Warning: Removed 3 rows containing missing values (position_stack).## Warning: Removed 20 rows containing missing values (geom_errorbar).
dsp_models_cols <- c("id", glb_model_evl_criteria)
if (glb_is_classification && glb_is_binomial)
dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
glb_models_df)[, dsp_models_cols])## id min.RMSE.fit max.Adj.R.sq.fit max.R.sq.fit
## 27 RFE.X.center.scale.rf 0.4791840 0.8875717665 0.897182567
## 26 RFE.X.YeoJohnson.rf 0.4794695 0.8872183523 0.896859364
## 17 RFE.X.rf 0.4813558 0.8875717665 0.897182567
## 16 RFE.X.gbm 0.5057258 0.7693114426 0.789031593
## 28 RFE.X.spatialSign.rf 0.5078949 0.9184073331 0.925382190
## 15 RFE.X.rpart 0.5186586 NA 0.738383665
## 10 Max.cor.Y.rpart 0.5425538 NA 0.661469678
## 9 Max.cor.Y.rcv.1X1.cp.0.rpart 0.5448116 NA 0.662176564
## 14 RFE.X.glmnet 0.6164891 0.5940573881 0.628758934
## 24 All.X.glmnet 0.6260497 0.4974337085 0.620643509
## 25 RFE.X.Interact.glmnet 0.6277709 0.5471574216 0.631152416
## 20 RFE.X.svmLinear 0.6324697 0.5690145632 0.575965941
## 22 RFE.X.earth 0.6332435 0.5852497794 0.620704234
## 21 RFE.X.svmLinear2 0.6335311 0.5702700433 0.577201172
## 13 RFE.X.glm 0.6340075 0.6213993720 0.645825219
## 12 Low.cor.X.glmnet 0.6377835 0.4713008397 0.576187931
## 23 RFE.X.bagEarth 0.6379618 0.8127487129 0.828755678
## 2 Max.cor.Y.rcv.1X1.glmnet 0.6473136 0.5215575671 0.523100930
## 8 Max.cor.Y.rcv.5X5.glmnet 0.6474622 0.5216616597 0.523204687
## 7 Max.cor.Y.rcv.5X3.glmnet 0.6479740 0.5216616597 0.523204687
## 3 Max.cor.Y.rcv.3X1.glmnet 0.6483723 0.5216600038 0.523203036
## 6 Max.cor.Y.rcv.5X1.glmnet 0.6486277 0.5216616597 0.523204687
## 4 Max.cor.Y.rcv.3X3.glmnet 0.6488737 0.5216626630 0.523205687
## 5 Max.cor.Y.rcv.3X5.glmnet 0.6491051 0.5216626630 0.523205687
## 11 Interact.High.cor.Y.glmnet 0.6508063 0.5166396765 0.523656197
## 19 RFE.X.avNNet 0.9285701 0.3487472073 0.359251285
## 18 RFE.X.nnet 0.9287422 0.3001577246 0.359982951
## 1 MFO.lm 0.9364496 0.0003064874 0.001918896print(myplot_radar(radar_inp_df=dsp_models_df))## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 28. Consider specifying shapes manually if you must have them.## Warning: Removed 3 rows containing missing values (geom_path).## Warning: Removed 89 rows containing missing values (geom_point).## Warning: Removed 3 rows containing missing values (geom_text).## Warning in RColorBrewer::brewer.pal(n, pal): n too large, allowed maximum for palette Set1 is 9
## Returning the palette you asked for with that many colors## Warning: The shape palette can deal with a maximum of 6 discrete values
## because more than 6 becomes difficult to discriminate; you have
## 28. Consider specifying shapes manually if you must have them.
print("Metrics used for model selection:"); print(model_sel_frmla)## [1] "Metrics used for model selection:"## ~+min.RMSE.fit - max.Adj.R.sq.fit - max.R.sq.fit
## <environment: 0x7fbb863e2dc0>print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))## [1] "Best model id: RFE.X.center.scale.rf"glb_get_predictions <- function(df, mdl_id, rsp_var_out, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
rsp_var_out <- paste0(rsp_var_out, mdl_id)
rsp_var_out <- paste0(glb_rsp_var, ".predict.")
predct_var_name <- paste0(rsp_var_out, mdl_id)
predct_accurate_var_name <- paste0(rsp_var_out, mdl_id, ".accurate")
predct_error_var_name <- paste0(rsp_var_out, mdl_id, ".err")
predct_erabs_var_name <- paste0(rsp_var_out, mdl_id, ".err.abs")
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glb_category_var), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glb_category_var), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glb_category_var), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")[, 2]
df[, rsp_var_out] <-
factor(levels(df[, glb_rsp_var])[
(df[, paste0(rsp_var_out, ".prob")] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# prediction stats already reported by myfit_mdl ???
}
if (glb_is_classification && !glb_is_binomial) {
df[, rsp_var_out] <- predict(mdl, newdata=df, type="raw")
df[, paste0(rsp_var_out, ".prob")] <-
predict(mdl, newdata=df, type="prob")
}
return(df)
}
#stop(here"); glb_to_sav(); glb_allobs_df <- sav_allobs_df; glb_trnobs_df <- sav_trnobs_df; glb_fitobs_df <- sav_fitobs_df; glb_OOBobs_df <- sav_OOBobs_df; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df
myget_category_stats <- function(obs_df, mdl_id, label) {
require(dplyr)
require(lazyeval)
predct_var_name <- paste0(glb_rsp_var_out, mdl_id)
predct_error_var_name <- paste0(glb_rsp_var_out, mdl_id, ".err.abs")
if (!predct_var_name %in% names(obs_df))
obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var_out)
tmp_obs_df <- obs_df %>%
dplyr::select_(glb_category_var, glb_rsp_var, predct_var_name, predct_error_var_name)
#dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
ret_ctgry_df <- tmp_obs_df %>%
dplyr::group_by_(glb_category_var) %>%
dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)),
interp(~sum(var), var=as.name(paste0("err.abs.", label))),
interp(~mean(var), var=as.name(paste0("err.abs.", label))),
interp(~n()))
names(ret_ctgry_df) <- c(glb_category_var,
#paste0(glb_rsp_var, ".abs.", label, ".sum"),
paste0("err.abs.", label, ".sum"),
paste0("err.abs.", label, ".mean"),
paste0(".n.", label))
ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
#colSums(ret_ctgry_df[, -grep(glb_category_var, names(ret_ctgry_df))])
return(ret_ctgry_df)
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glb_fitobs_df, mdl_id="", label="fit"))[, -grep(glb_category_var, names(ctgry_df))]))
if (!is.null(glb_mdl_ensemble)) {
mdl_id_pfx <- "Ensemble"
if (#(glb_is_regression) |
((glb_is_classification) & (!glb_is_binomial)))
stop("Ensemble models not implemented yet for multinomial classification")
if (glb_mdl_ensemble == "auto") {
mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")
tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
row.names(tmp_models_df) <- tmp_models_df$id
# mdl_threshold_pos <- min(which(tmp_models_df$id %in%
# c("MFO.myMFO_classfr", "Baseline.mybaseln_classfr"))) - 1
mdl_threshold_pos <-
min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
glb_mdl_ensemble <- tmp_models_df$id[1:mdl_threshold_pos]
}
for (mdl_id in glb_mdl_ensemble) {
glb_fitobs_df <- glb_get_predictions(df=glb_fitobs_df, mdl_id, glb_rsp_var_out)
glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id, glb_rsp_var_out)
}
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(glb_rsp_var_out, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
#cor_df <- data.frame(cor=cor(glb_fitobs_df[, glb_rsp_var], glb_fitobs_df[, paste(glb_rsp_var_out, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glb_fitobs_df <- glb_get_predictions(df=glb_fitobs_df, "Ensemble.glmnet", glb_rsp_var_out);print(colSums((ctgry_df <- myget_category_stats(obs_df=glb_fitobs_df, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glb_category_var, names(ctgry_df))]))
### bid0_sp
# Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
# old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
# RFE only ; models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
# RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
# RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
# RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
# RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
# RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
# RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
### bid0_sp
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
### bid1_sp
indep_vars <- paste(glb_rsp_var_out, glb_mdl_ensemble, sep = "")
if (glb_is_classification)
indep_vars <- paste(indep_vars, ".prob", sep = "")
# indep_vars <- grep(glb_rsp_var_out, names(glb_fitobs_df), fixed=TRUE, value=TRUE)
# if (glb_is_regression)
# indep_vars <- indep_vars[!grepl("(err\\.abs|accurate)$", indep_vars)]
# if (glb_is_classification && glb_is_binomial)
# indep_vars <- grep("prob$", indep_vars, value=TRUE) else
# indep_vars <- indep_vars[!grepl("err$", indep_vars)]
#rfe_fit_ens_results <- myrun_rfe(glb_fitobs_df, indep_vars)
for (method in c("glmnet")) {
#sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
#glb_models_df <- sav_models_df; print(glb_models_df$id)
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type="regression", tune.df=NULL,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
train.method=method)),
indep_vars=indep_vars, rsp_var=glb_rsp_var,
fit_df=glb_fitobs_df, OOB_df=glb_OOBobs_df)
}
dsp_models_df <- get_dsp_models_df()
}## Warning in if (glb_mdl_ensemble == "auto") {: the condition has length > 1
## and only the first element will be used## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## [1] "fitting model: Ensemble.glmnet"
## [1] " indep_vars: startprice.log10.predict.RFE.X.spatialSign.rf,startprice.log10.predict.RFE.X.YeoJohnson.rf,startprice.log10.predict.RFE.X.center.scale.rf,startprice.log10.predict.RFE.X.rf,startprice.log10.predict.RFE.X.avNNet,startprice.log10.predict.RFE.X.bagEarth,startprice.log10.predict.RFE.X.earth,startprice.log10.predict.RFE.X.gbm,startprice.log10.predict.RFE.X.glmnet,startprice.log10.predict.RFE.X.nnet,startprice.log10.predict.RFE.X.svmLinear2,startprice.log10.predict.RFE.X.glm,startprice.log10.predict.RFE.X.svmLinear,startprice.log10.predict.RFE.X.rpart"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 1, lambda = 0.000839 on full training set## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 87 -none- numeric
## beta 1218 dgCMatrix S4
## df 87 -none- numeric
## dim 2 -none- numeric
## lambda 87 -none- numeric
## dev.ratio 87 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 14 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -2.798455e-02
## startprice.log10.predict.RFE.X.YeoJohnson.rf
## 1.352033e-01
## startprice.log10.predict.RFE.X.bagEarth
## -5.109589e-02
## startprice.log10.predict.RFE.X.center.scale.rf
## 2.764652e-02
## startprice.log10.predict.RFE.X.earth
## 8.388585e-03
## startprice.log10.predict.RFE.X.gbm
## -3.643590e-02
## startprice.log10.predict.RFE.X.nnet
## -1.176578e-01
## startprice.log10.predict.RFE.X.rf
## 2.565127e-01
## startprice.log10.predict.RFE.X.rpart
## -3.346331e-01
## startprice.log10.predict.RFE.X.spatialSign.rf
## 1.161056e+00
## startprice.log10.predict.RFE.X.svmLinear
## -2.347529e-05
## startprice.log10.predict.RFE.X.svmLinear2
## -8.379674e-02
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -0.02871804
## startprice.log10.predict.RFE.X.YeoJohnson.rf
## 0.13766000
## startprice.log10.predict.RFE.X.bagEarth
## -0.05633547
## startprice.log10.predict.RFE.X.center.scale.rf
## 0.03026594
## startprice.log10.predict.RFE.X.earth
## 0.01165288
## startprice.log10.predict.RFE.X.gbm
## -0.03752383
## startprice.log10.predict.RFE.X.nnet
## -0.11766482
## startprice.log10.predict.RFE.X.rf
## 0.25929394
## startprice.log10.predict.RFE.X.rpart
## -0.33468435
## startprice.log10.predict.RFE.X.spatialSign.rf
## 1.15914032
## startprice.log10.predict.RFE.X.svmLinear2
## -0.08609692
## [1] " calling mypredict_mdl for fit:"
## [1] " calling mypredict_mdl for OOB:"
## id
## 1 Ensemble.glmnet
## feats
## 1 startprice.log10.predict.RFE.X.spatialSign.rf,startprice.log10.predict.RFE.X.YeoJohnson.rf,startprice.log10.predict.RFE.X.center.scale.rf,startprice.log10.predict.RFE.X.rf,startprice.log10.predict.RFE.X.avNNet,startprice.log10.predict.RFE.X.bagEarth,startprice.log10.predict.RFE.X.earth,startprice.log10.predict.RFE.X.gbm,startprice.log10.predict.RFE.X.glmnet,startprice.log10.predict.RFE.X.nnet,startprice.log10.predict.RFE.X.svmLinear2,startprice.log10.predict.RFE.X.glm,startprice.log10.predict.RFE.X.svmLinear,startprice.log10.predict.RFE.X.rpart
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 25 1.985 0.006
## max.R.sq.fit min.RMSE.fit max.Adj.R.sq.fit max.R.sq.OOB min.RMSE.OOB
## 1 0.9470764 0.2210626 0.9458537 0.1980399 1.04059
## max.Adj.R.sq.OOB max.Rsquared.fit min.RMSESD.fit max.RsquaredSD.fit
## 1 0.1785139 0.9444287 0.03009502 0.01364212
## [1] "Cross Validation issues:"## Warning in get_dsp_models_df(): Cross Validation issues:## MFO.lm Max.cor.Y.rcv.1X1.glmnet
## 0 0
## Max.cor.Y.rcv.1X1.cp.0.rpart RFE.X.gbm
## 0 1
## RFE.X.bagEarth
## 1if (is.null(glb_sel_mdl_id))
glb_sel_mdl_id <- dsp_models_df[1, "id"] else
print(sprintf("User specified selection: %s", glb_sel_mdl_id))
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glb_sel_mdl_id]])
## Length Class Mode
## a0 87 -none- numeric
## beta 1218 dgCMatrix S4
## df 87 -none- numeric
## dim 2 -none- numeric
## lambda 87 -none- numeric
## dev.ratio 87 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 14 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 1 -none- logical
## [1] "min lambda > lambdaOpt:"
## (Intercept)
## -2.798455e-02
## startprice.log10.predict.RFE.X.YeoJohnson.rf
## 1.352033e-01
## startprice.log10.predict.RFE.X.bagEarth
## -5.109589e-02
## startprice.log10.predict.RFE.X.center.scale.rf
## 2.764652e-02
## startprice.log10.predict.RFE.X.earth
## 8.388585e-03
## startprice.log10.predict.RFE.X.gbm
## -3.643590e-02
## startprice.log10.predict.RFE.X.nnet
## -1.176578e-01
## startprice.log10.predict.RFE.X.rf
## 2.565127e-01
## startprice.log10.predict.RFE.X.rpart
## -3.346331e-01
## startprice.log10.predict.RFE.X.spatialSign.rf
## 1.161056e+00
## startprice.log10.predict.RFE.X.svmLinear
## -2.347529e-05
## startprice.log10.predict.RFE.X.svmLinear2
## -8.379674e-02
## [1] "max lambda < lambdaOpt:"
## (Intercept)
## -0.02871804
## startprice.log10.predict.RFE.X.YeoJohnson.rf
## 0.13766000
## startprice.log10.predict.RFE.X.bagEarth
## -0.05633547
## startprice.log10.predict.RFE.X.center.scale.rf
## 0.03026594
## startprice.log10.predict.RFE.X.earth
## 0.01165288
## startprice.log10.predict.RFE.X.gbm
## -0.03752383
## startprice.log10.predict.RFE.X.nnet
## -0.11766482
## startprice.log10.predict.RFE.X.rf
## 0.25929394
## startprice.log10.predict.RFE.X.rpart
## -0.33468435
## startprice.log10.predict.RFE.X.spatialSign.rf
## 1.15914032
## startprice.log10.predict.RFE.X.svmLinear2
## -0.08609692## [1] TRUE#stop(here"); glb_to_sav()
# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glb_sel_mdl_id))## [1] "Ensemble.glmnet fit prediction diagnostics:"glb_fitobs_df <- glb_get_predictions(df=glb_fitobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)
print(sprintf("%s OOB prediction diagnostics:", glb_sel_mdl_id))## [1] "Ensemble.glmnet OOB prediction diagnostics:"glb_OOBobs_df <- glb_get_predictions(df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
rsp_var_out=glb_rsp_var_out)
glb_featsimp_df <-
myget_feats_importance(mdl=glb_sel_mdl, featsimp_df=NULL)
glb_featsimp_df[, paste0(glb_sel_mdl_id, ".importance")] <- glb_featsimp_df$importance
#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".importance")] <- glb_featsimp_df$importance; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.importance) | (abs(RFE.X.YeoJohnson.glmnet.importance - RFE.X.glmnet.importance) > 0.0001), select=-importance)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))
print(glb_featsimp_df)## importance
## startprice.log10.predict.RFE.X.spatialSign.rf 100.00000
## startprice.log10.predict.RFE.X.rf 39.53565
## startprice.log10.predict.RFE.X.YeoJohnson.rf 31.42341
## startprice.log10.predict.RFE.X.center.scale.rf 24.23239
## startprice.log10.predict.RFE.X.earth 22.94697
## startprice.log10.predict.RFE.X.avNNet 22.37479
## startprice.log10.predict.RFE.X.glm 22.37479
## startprice.log10.predict.RFE.X.glmnet 22.37479
## startprice.log10.predict.RFE.X.svmLinear 22.37331
## startprice.log10.predict.RFE.X.gbm 19.93481
## startprice.log10.predict.RFE.X.bagEarth 18.94024
## startprice.log10.predict.RFE.X.svmLinear2 16.76393
## startprice.log10.predict.RFE.X.nnet 14.50780
## startprice.log10.predict.RFE.X.rpart 0.00000
## Ensemble.glmnet.importance
## startprice.log10.predict.RFE.X.spatialSign.rf 100.00000
## startprice.log10.predict.RFE.X.rf 39.53565
## startprice.log10.predict.RFE.X.YeoJohnson.rf 31.42341
## startprice.log10.predict.RFE.X.center.scale.rf 24.23239
## startprice.log10.predict.RFE.X.earth 22.94697
## startprice.log10.predict.RFE.X.avNNet 22.37479
## startprice.log10.predict.RFE.X.glm 22.37479
## startprice.log10.predict.RFE.X.glmnet 22.37479
## startprice.log10.predict.RFE.X.svmLinear 22.37331
## startprice.log10.predict.RFE.X.gbm 19.93481
## startprice.log10.predict.RFE.X.bagEarth 18.94024
## startprice.log10.predict.RFE.X.svmLinear2 16.76393
## startprice.log10.predict.RFE.X.nnet 14.50780
## startprice.log10.predict.RFE.X.rpart 0.00000# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
if (!is.null(featsimp_df <- glb_featsimp_df)) {
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <- ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <- gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -importance.max, summaryBy(importance ~ feat + feat.interact,
data=featsimp_df, FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
featsimp_df <- subset(featsimp_df, !is.na(importance.max))
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ", nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- paste0(glb_rsp_var_out, mdl_id)
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars=var,
measure.vars=c(glb_rsp_var, rsp_var_out))
# if (var == "<feat_name>") print(myplot_scatter(plot_df, var, "value",
# facet_colcol_name="variable") +
# geom_vline(xintercept=<divider_val>, linetype="dotted")) else
print(myplot_scatter(plot_df, var, "value", colorcol_name="variable",
facet_colcol_name="variable", jitter=TRUE) +
guides(color=FALSE))
}
}
if (glb_is_regression) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glb_id_var)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var,
rsp_var_out=rsp_var_out,
id_vars=glb_id_var,
prob_threshold=prob_threshold)
# + geom_hline(yintercept=<divider_val>, linetype = "dotted")
)
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id,
prob_threshold=glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_OOBobs_df, mdl_id =
## glb_sel_mdl_id): Limiting important feature scatter plots to 5 out of 14
## UniqueID
## 883 11974
## 665 11330
## 1051 12215
## 966 12084
## 842 11891
## description
## 883
## 665
## 1051 The iPad is in great shape, having been gently used and covered since purchase. The Speck cover is
## 966 Minor wear and tear. Device is fully operational and in working order. No major scratches on screen
## 842
## biddable startprice condition cellular carrier color
## 883 1 0.01 New 0 None Unknown
## 665 1 0.01 New Unknown Unknown White
## 1051 1 0.01 Used 0 None Black
## 966 1 0.01 Used 0 None White
## 842 1 0.01 Seller refurbished 0 None Unknown
## storage productline sold .src .grpid startprice.log10 .rnorm
## 883 16 iPad Air 2 NA Test <NA> -2 0.2019409
## 665 16 iPad mini 3 1 Train <NA> -2 1.5773727
## 1051 16 iPad mini NA Test <NA> -2 -0.6645831
## 966 16 iPad 2 NA Test <NA> -2 0.4274970
## 842 64 iPad 4 NA Test <NA> -2 3.6361055
## startprice.log10.cut.fctr
## 883 (-2,-0.352]
## 665 (-2,-0.352]
## 1051 (-2,-0.352]
## 966 (-2,-0.352]
## 842 (-2,-0.352]
## descr.my
## 883
## 665
## 1051 The iPad is in great shape, having been gently used and covered since purchase. The Speck cover is
## 966 Minor wear and tear. Device is fully operational and in working order. No major scratches on screen
## 842
## condition.fctr cellular.fctr carrier.fctr color.fctr storage.fctr
## 883 New 0 None Unknown 16
## 665 New Unknown Unknown White 16
## 1051 Used 0 None Black 16
## 966 Used 0 None White 16
## 842 Seller refurbished 0 None Unknown 64
## D.terms.post.stop.n D.terms.post.stop.n.log D.weight.post.stop.sum
## 883 0 0.000000 0.000000
## 665 0 0.000000 0.000000
## 1051 5 1.791759 6.438998
## 966 11 2.484907 5.577275
## 842 0 0.000000 0.000000
## D.terms.post.stem.n D.terms.post.stem.n.log D.weight.post.stem.sum
## 883 0 0.000000 0.000000
## 665 0 0.000000 0.000000
## 1051 5 1.791759 6.390390
## 966 11 2.484907 5.469383
## 842 0 0.000000 0.000000
## D.terms.n.stem.stop.Ratio D.weight.sum.stem.stop.Ratio D.T.condit
## 883 1 1.0000000 0
## 665 1 1.0000000 0
## 1051 1 0.9924509 0
## 966 1 0.9806549 0
## 842 1 1.0000000 0
## D.T.use D.T.new D.T.screen D.T.scratch D.T.good D.T.great D.T.excel
## 883 0.0000000 0 0.0000000 0.0000000 0 0.0000000 0
## 665 0.0000000 0 0.0000000 0.0000000 0 0.0000000 0
## 1051 0.6461512 0 0.0000000 0.0000000 0 0.8974191 0
## 966 0.0000000 0 0.3468835 0.3207034 0 0.0000000 0
## 842 0.0000000 0 0.0000000 0.0000000 0 0.0000000 0
## D.T.work D.T.no D.T.function. D.T.like D.T.crack D.T.fulli D.T.box
## 883 0.0000000 0.000000 0 0 0 0.0000000 0
## 665 0.0000000 0.000000 0 0 0 0.0000000 0
## 1051 0.0000000 0.000000 0 0 0 0.0000000 0
## 966 0.3683781 0.363096 0 0 0 0.4610962 0
## 842 0.0000000 0.000000 0 0 0 0.0000000 0
## D.T.mint D.T.item D.T.lock D.T.see D.T.icloud D.T.perfect
## 883 0 0 0 0 0 0
## 665 0 0 0 0 0 0
## 1051 0 0 0 0 0 0
## 966 0 0 0 0 0 0
## 842 0 0 0 0 0 0
## D.wrds.n.log D.wrds.unq.n.log D.weight.sum D.ratio.weight.sum.wrds.n
## 883 0.000000 0.000000 0.000000 0.0000000
## 665 0.000000 0.000000 0.000000 0.0000000
## 1051 2.944439 1.791759 6.390390 0.3550216
## 966 2.890372 2.484907 5.469383 0.3217284
## 842 0.000000 0.000000 0.000000 0.0000000
## D.chrs.n.log D.chrs.uppr.n.log D.dgts.n.log D.chrs.pnct01.n.log
## 883 0.000000 0.000000 0 0
## 665 0.000000 0.000000 0 0
## 1051 4.615121 4.382027 0 0
## 966 4.615121 4.406719 0 0
## 842 0.000000 0.000000 0 0
## D.chrs.pnct02.n.log D.chrs.pnct03.n.log D.chrs.pnct04.n.log
## 883 0 0 0
## 665 0 0 0
## 1051 0 0 0
## 966 0 0 0
## 842 0 0 0
## D.chrs.pnct05.n.log D.chrs.pnct06.n.log D.chrs.pnct07.n.log
## 883 0 0 0
## 665 0 0 0
## 1051 0 0 0
## 966 0 0 0
## 842 0 0 0
## D.chrs.pnct08.n.log D.chrs.pnct09.n.log D.chrs.pnct10.n.log
## 883 0 0 0
## 665 0 0 0
## 1051 0 0 0
## 966 0 0 0
## 842 0 0 0
## D.chrs.pnct11.n.log D.chrs.pnct12.n.log D.chrs.pnct13.n.log
## 883 0.0000000 0 0.0000000
## 665 0.0000000 0 0.0000000
## 1051 0.6931472 0 0.6931472
## 966 0.0000000 0 1.0986123
## 842 0.0000000 0 0.0000000
## D.chrs.pnct14.n.log D.chrs.pnct15.n.log D.chrs.pnct16.n.log
## 883 0 0 0
## 665 0 0 0
## 1051 0 0 0
## 966 0 0 0
## 842 0 0 0
## D.chrs.pnct17.n.log D.chrs.pnct18.n.log D.chrs.pnct19.n.log
## 883 0 0 0
## 665 0 0 0
## 1051 0 0 0
## 966 0 0 0
## 842 0 0 0
## D.chrs.pnct20.n.log D.chrs.pnct21.n.log D.chrs.pnct22.n.log
## 883 0 0 0
## 665 0 0 0
## 1051 0 0 0
## 966 0 0 0
## 842 0 0 0
## D.chrs.pnct23.n.log D.chrs.pnct24.n.log D.chrs.pnct25.n.log
## 883 0 0 0
## 665 0 0 0
## 1051 0 0 0
## 966 0 0 0
## 842 0 0 0
## D.chrs.pnct26.n.log D.chrs.pnct27.n.log D.chrs.pnct28.n.log
## 883 0 0 0
## 665 0 0 0
## 1051 0 0 0
## 966 0 0 0
## 842 0 0 0
## D.chrs.pnct29.n.log D.chrs.pnct30.n.log D.wrds.stop.n.log
## 883 0 0 0.000000
## 665 0 0 0.000000
## 1051 0 0 2.564949
## 966 0 0 1.945910
## 842 0 0 0.000000
## D.ratio.wrds.stop.n.wrds.n D.P.http D.P.mini D.P.air D.P.black
## 883 1.0000000 0 0 0 0
## 665 1.0000000 0 0 0 0
## 1051 0.6842105 0 0 0 0
## 966 0.3888889 0 0 0 0
## 842 1.0000000 0 0 0 0
## D.P.white D.P.gold D.P.spacegray prdl.descr.my.fctr
## 883 0 0 0 iPadAir2#0
## 665 0 0 0 iPadmini3#0
## 1051 0 0 0 iPadmini#1
## 966 0 0 0 iPad2#1
## 842 0 0 0 iPad4#0
## startprice.dgt1.is9 startprice.dgt2.is9 startprice.dgt3.is9
## 883 0 0 0
## 665 0 0 0
## 1051 0 0 0
## 966 0 0 0
## 842 0 0 0
## startprice.dcm1.is9 startprice.dcm2.is9 .clusterid .clusterid.fctr
## 883 0 0 1 1
## 665 0 0 1 1
## 1051 0 0 2 2
## 966 0 0 3 3
## 842 0 0 1 1
## startprice.log10.predict.RFE.X.spatialSign.rf
## 883 2.384565
## 665 2.337335
## 1051 2.033334
## 966 1.986233
## 842 1.956806
## startprice.log10.predict.RFE.X.spatialSign.rf.err
## 883 4.384565
## 665 4.337335
## 1051 4.033334
## 966 3.986233
## 842 3.956806
## startprice.log10.predict.RFE.X.spatialSign.rf.err.abs
## 883 4.384565
## 665 4.337335
## 1051 4.033334
## 966 3.986233
## 842 3.956806
## startprice.log10.predict.RFE.X.spatialSign.rf.accurate
## 883 FALSE
## 665 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## startprice.log10.predict.RFE.X.YeoJohnson.rf
## 883 2.385705
## 665 2.070178
## 1051 1.929887
## 966 2.006112
## 842 2.016833
## startprice.log10.predict.RFE.X.YeoJohnson.rf.err
## 883 4.385705
## 665 4.070178
## 1051 3.929887
## 966 4.006112
## 842 4.016833
## startprice.log10.predict.RFE.X.YeoJohnson.rf.err.abs
## 883 4.385705
## 665 4.070178
## 1051 3.929887
## 966 4.006112
## 842 4.016833
## startprice.log10.predict.RFE.X.YeoJohnson.rf.accurate
## 883 FALSE
## 665 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## startprice.log10.predict.RFE.X.center.scale.rf
## 883 2.385705
## 665 2.070178
## 1051 1.929589
## 966 2.005458
## 842 2.016833
## startprice.log10.predict.RFE.X.center.scale.rf.err
## 883 4.385705
## 665 4.070178
## 1051 3.929589
## 966 4.005458
## 842 4.016833
## startprice.log10.predict.RFE.X.center.scale.rf.err.abs
## 883 4.385705
## 665 4.070178
## 1051 3.929589
## 966 4.005458
## 842 4.016833
## startprice.log10.predict.RFE.X.center.scale.rf.accurate
## 883 FALSE
## 665 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## startprice.log10.predict.RFE.X.rf
## 883 2.385705
## 665 2.070178
## 1051 1.929589
## 966 2.005458
## 842 2.016833
## startprice.log10.predict.RFE.X.rf.err
## 883 4.385705
## 665 4.070178
## 1051 3.929589
## 966 4.005458
## 842 4.016833
## startprice.log10.predict.RFE.X.rf.err.abs
## 883 4.385705
## 665 4.070178
## 1051 3.929589
## 966 4.005458
## 842 4.016833
## startprice.log10.predict.RFE.X.rf.accurate
## 883 FALSE
## 665 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## startprice.log10.predict.RFE.X.avNNet
## 883 0.9988713
## 665 0.9990158
## 1051 0.9993395
## 966 0.9996110
## 842 0.9987748
## startprice.log10.predict.RFE.X.avNNet.err
## 883 2.998871
## 665 2.999016
## 1051 2.999339
## 966 2.999611
## 842 2.998775
## startprice.log10.predict.RFE.X.avNNet.err.abs
## 883 2.998871
## 665 2.999016
## 1051 2.999339
## 966 2.999611
## 842 2.998775
## startprice.log10.predict.RFE.X.avNNet.accurate
## 883 FALSE
## 665 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## startprice.log10.predict.RFE.X.bagEarth
## 883 2.335926
## 665 1.956904
## 1051 1.931170
## 966 2.012768
## 842 1.964666
## startprice.log10.predict.RFE.X.bagEarth.err
## 883 4.335926
## 665 3.956904
## 1051 3.931170
## 966 4.012768
## 842 3.964666
## startprice.log10.predict.RFE.X.bagEarth.err.abs
## 883 4.335926
## 665 3.956904
## 1051 3.931170
## 966 4.012768
## 842 3.964666
## startprice.log10.predict.RFE.X.bagEarth.accurate
## 883 FALSE
## 665 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## startprice.log10.predict.RFE.X.earth
## 883 2.200465
## 665 1.779266
## 1051 1.779266
## 966 1.779266
## 842 1.779266
## startprice.log10.predict.RFE.X.earth.err
## 883 4.200465
## 665 3.779266
## 1051 3.779266
## 966 3.779266
## 842 3.779266
## startprice.log10.predict.RFE.X.earth.err.abs
## 883 4.200465
## 665 3.779266
## 1051 3.779266
## 966 3.779266
## 842 3.779266
## startprice.log10.predict.RFE.X.earth.accurate
## 883 FALSE
## 665 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## startprice.log10.predict.RFE.X.gbm
## 883 2.264936
## 665 1.917832
## 1051 2.028287
## 966 2.035972
## 842 2.002162
## startprice.log10.predict.RFE.X.gbm.err
## 883 4.264936
## 665 3.917832
## 1051 4.028287
## 966 4.035972
## 842 4.002162
## startprice.log10.predict.RFE.X.gbm.err.abs
## 883 4.264936
## 665 3.917832
## 1051 4.028287
## 966 4.035972
## 842 4.002162
## startprice.log10.predict.RFE.X.gbm.accurate
## 883 FALSE
## 665 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## startprice.log10.predict.RFE.X.glmnet
## 883 2.087784
## 665 1.886334
## 1051 1.759549
## 966 1.773414
## 842 1.644223
## startprice.log10.predict.RFE.X.glmnet.err
## 883 4.087784
## 665 3.886334
## 1051 3.759549
## 966 3.773414
## 842 3.644223
## startprice.log10.predict.RFE.X.glmnet.err.abs
## 883 4.087784
## 665 3.886334
## 1051 3.759549
## 966 3.773414
## 842 3.644223
## startprice.log10.predict.RFE.X.glmnet.accurate
## 883 FALSE
## 665 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## startprice.log10.predict.RFE.X.nnet
## 883 0.9986147
## 665 0.9988585
## 1051 0.9987734
## 966 0.9994974
## 842 0.9988754
## startprice.log10.predict.RFE.X.nnet.err
## 883 2.998615
## 665 2.998858
## 1051 2.998773
## 966 2.999497
## 842 2.998875
## startprice.log10.predict.RFE.X.nnet.err.abs
## 883 2.998615
## 665 2.998858
## 1051 2.998773
## 966 2.999497
## 842 2.998875
## startprice.log10.predict.RFE.X.nnet.accurate
## 883 FALSE
## 665 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## startprice.log10.predict.RFE.X.svmLinear2
## 883 2.401182
## 665 1.928004
## 1051 1.869113
## 966 1.932000
## 842 1.800848
## startprice.log10.predict.RFE.X.svmLinear2.err
## 883 4.401182
## 665 3.928004
## 1051 3.869113
## 966 3.932000
## 842 3.800848
## startprice.log10.predict.RFE.X.svmLinear2.err.abs
## 883 4.401182
## 665 3.928004
## 1051 3.869113
## 966 3.932000
## 842 3.800848
## startprice.log10.predict.RFE.X.svmLinear2.accurate
## 883 FALSE
## 665 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## startprice.log10.predict.RFE.X.glm
## 883 2.228872
## 665 1.824446
## 1051 1.822275
## 966 1.936295
## 842 1.678870
## startprice.log10.predict.RFE.X.glm.err
## 883 4.228872
## 665 3.824446
## 1051 3.822275
## 966 3.936295
## 842 3.678870
## startprice.log10.predict.RFE.X.glm.err.abs
## 883 4.228872
## 665 3.824446
## 1051 3.822275
## 966 3.936295
## 842 3.678870
## startprice.log10.predict.RFE.X.glm.accurate
## 883 FALSE
## 665 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## startprice.log10.predict.RFE.X.svmLinear
## 883 2.398344
## 665 1.963542
## 1051 1.870308
## 966 1.940720
## 842 1.810945
## startprice.log10.predict.RFE.X.svmLinear.err
## 883 4.398344
## 665 3.963542
## 1051 3.870308
## 966 3.940720
## 842 3.810945
## startprice.log10.predict.RFE.X.svmLinear.err.abs
## 883 4.398344
## 665 3.963542
## 1051 3.870308
## 966 3.940720
## 842 3.810945
## startprice.log10.predict.RFE.X.svmLinear.accurate
## 883 FALSE
## 665 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## startprice.log10.predict.RFE.X.rpart
## 883 2.311259
## 665 2.223889
## 1051 1.955542
## 966 1.955542
## 842 1.955542
## startprice.log10.predict.RFE.X.rpart.err
## 883 4.311259
## 665 4.223889
## 1051 3.955542
## 966 3.955542
## 842 3.955542
## startprice.log10.predict.RFE.X.rpart.err.abs
## 883 4.311259
## 665 4.223889
## 1051 3.955542
## 966 3.955542
## 842 3.955542
## startprice.log10.predict.RFE.X.rpart.accurate
## 883 FALSE
## 665 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## startprice.log10.predict.Ensemble.glmnet
## 883 2.465512
## 665 2.375689
## 1051 2.055826
## 966 2.023203
## 842 2.008512
## startprice.log10.predict.Ensemble.glmnet.err
## 883 4.465512
## 665 4.375689
## 1051 4.055826
## 966 4.023203
## 842 4.008512
## startprice.log10.predict.Ensemble.glmnet.err.abs
## 883 4.465512
## 665 4.375689
## 1051 4.055826
## 966 4.023203
## 842 4.008512
## startprice.log10.predict.Ensemble.glmnet.accurate .label
## 883 FALSE 11974
## 665 FALSE 11330
## 1051 FALSE 12215
## 966 FALSE 12084
## 842 FALSE 11891
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_fitobs_df, mdl_id=glb_sel_mdl_id, label="fit"),
by=glb_category_var, all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_OOBobs_df, mdl_id=glb_sel_mdl_id, label="OOB"),
by=glb_category_var, all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
if (any(grepl("OOB", glb_model_evl_criteria)))
print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df)) else
print(orderBy(~-err.abs.fit.mean, glb_ctgry_df))## prdl.descr.my.fctr .n.Tst .n.OOB.x .freqRatio.Tst
## iPadmini3#1 iPadmini3#1 5 5 0.008726003
## iPad3#0 iPad3#0 27 27 0.047120419
## iPadmini2#1 iPadmini2#1 18 19 0.031413613
## iPadAir#0 iPadAir#0 29 29 0.050610820
## iPadAir2#0 iPadAir2#0 32 32 0.055846422
## iPadAir#1 iPadAir#1 20 20 0.034904014
## Unknown#0 Unknown#0 34 34 0.059336824
## iPad3#1 iPad3#1 10 11 0.017452007
## iPad4#1 iPad4#1 25 25 0.043630017
## iPadmini2#0 iPadmini2#0 28 29 0.048865620
## iPadmini3#0 iPadmini3#0 21 22 0.036649215
## iPad2#0 iPad2#0 68 71 0.118673647
## Unknown#1 Unknown#1 32 32 0.055846422
## iPadmini#1 iPadmini#1 34 35 0.059336824
## iPad1#1 iPad1#1 30 31 0.052356021
## iPad4#0 iPad4#0 20 21 0.034904014
## iPadAir2#1 iPadAir2#1 9 9 0.015706806
## iPadmini#0 iPadmini#0 62 63 0.108202443
## iPad1#0 iPad1#0 42 43 0.073298429
## iPad2#1 iPad2#1 27 32 0.047120419
## .freqRatio.OOB err.abs.fit.sum err.abs.fit.mean .n.fit
## iPadmini3#1 0.008474576 1.006188 0.33539603 3
## iPad3#0 0.045762712 10.534039 0.24497764 43
## iPadmini2#1 0.032203390 2.464464 0.22404221 11
## iPadAir#0 0.049152542 6.906065 0.20927469 33
## iPadAir2#0 0.054237288 6.534992 0.18671406 35
## iPadAir#1 0.033898305 4.416322 0.15772578 28
## Unknown#0 0.057627119 4.773216 0.14916300 32
## iPad3#1 0.018644068 2.430834 0.12793862 19
## iPad4#1 0.042372881 2.025938 0.12662115 16
## iPadmini2#0 0.049152542 2.627509 0.12511945 21
## iPadmini3#0 0.037288136 1.731655 0.12368964 14
## iPad2#0 0.120338983 6.961378 0.10877153 64
## Unknown#1 0.054237288 2.594689 0.10811205 24
## iPadmini#1 0.059322034 4.102736 0.10519837 39
## iPad1#1 0.052542373 4.069636 0.10174089 40
## iPad4#0 0.035593220 2.666249 0.09522319 28
## iPadAir2#1 0.015254237 1.476058 0.09225365 16
## iPadmini#0 0.106779661 4.928832 0.08497986 58
## iPad1#0 0.072881356 4.323815 0.08007064 54
## iPad2#1 0.054237288 3.249884 0.07557870 43
## err.abs.OOB.sum err.abs.OOB.mean .n.OOB.y
## iPadmini3#1 7.685565 1.5371130 5
## iPad3#0 13.130682 0.4863216 27
## iPadmini2#1 9.406429 0.4950752 19
## iPadAir#0 19.008714 0.6554729 29
## iPadAir2#0 17.224364 0.5382614 32
## iPadAir#1 12.156288 0.6078144 20
## Unknown#0 21.872608 0.6433120 34
## iPad3#1 7.360733 0.6691575 11
## iPad4#1 15.475946 0.6190379 25
## iPadmini2#0 14.331345 0.4941843 29
## iPadmini3#0 13.593255 0.6178752 22
## iPad2#0 40.268731 0.5671652 71
## Unknown#1 15.703961 0.4907488 32
## iPadmini#1 25.720908 0.7348831 35
## iPad1#1 15.224456 0.4911115 31
## iPad4#0 17.721903 0.8439001 21
## iPadAir2#1 2.199368 0.2443742 9
## iPadmini#0 30.029142 0.4766531 63
## iPad1#0 27.125496 0.6308255 43
## iPad2#1 35.951318 1.1234787 32print(colSums(glb_ctgry_df[, -grep(glb_category_var, names(glb_ctgry_df))]))## .n.Tst .n.OOB.x .freqRatio.Tst .freqRatio.OOB
## 573.000000 590.000000 1.000000 1.000000
## err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## 79.824499 2.862591 621.000000 361.191213
## err.abs.OOB.mean .n.OOB.y
## 12.966766 590.000000write.csv(glb_OOBobs_df[, c(glb_id_var,
grep(glb_rsp_var, names(glb_OOBobs_df), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glb_out_pfx, glb_sel_mdl_id), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)## label step_major step_minor label_minor bgn end elapsed
## 12 fit.models 7 2 2 414.728 460.951 46.223
## 13 fit.models 7 3 3 460.952 NA NA# if (sum(is.na(glb_allobs_df$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb_to_sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glb_fitobs_df), names(glb_trnobs_df)))
glb_trnobs_df[glb_trnobs_df$.lcn == "Fit", col] <<- glb_fitobs_df[, col]
for (col in setdiff(names(glb_fitobs_df), names(glb_allobs_df)))
glb_allobs_df[glb_allobs_df$.lcn == "Fit", col] <<- glb_fitobs_df[, col]
if (all(is.na(glb_newobs_df[, glb_rsp_var])))
for (col in setdiff(names(glb_OOBobs_df), names(glb_trnobs_df)))
glb_trnobs_df[glb_trnobs_df$.lcn == "OOB", col] <<- glb_OOBobs_df[, col]
for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <<- glb_OOBobs_df[, col]
}
sync_glb_obs_df()
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df,
glb_allobs_df, #glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_sel_mdl, glb_sel_mdl_id,
glb_model_type,
file=paste0(glb_out_pfx, "selmdl_dsk.RData"))
#load(paste0(glb_out_pfx, "selmdl_dsk.RData"))
rm(ret_lst)
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 13 fit.models 7 3 3 460.952 466.77
## 14 fit.data.training 8 0 0 466.771 NA
## elapsed
## 13 5.819
## 14 NAStep 8.0: fit data training
#load(paste0(glb_inp_pfx, "dsk.RData"))
if (!is.null(glb_fin_mdl_id) && (glb_fin_mdl_id %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_models_lst[[glb_fin_mdl_id]]
} else if (nrow(glb_fitobs_df) + length(glb_obsfit_outliers) == nrow(glb_trnobs_df)) {
warning("Final model same as glb_sel_mdl_id")
glb_fin_mdl_id <- paste0("Final.", glb_sel_mdl_id)
glb_fin_mdl <- glb_sel_mdl
glb_models_lst[[glb_fin_mdl_id]] <- glb_fin_mdl
} else {
if (grepl("RFE", glb_sel_mdl_id) || grepl("RFE", glb_mdl_ensemble))
stop("RFE indep_vars should be based on glb_trnobs_df. Also, outliers in OOB.\nNot implemented yet")
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), importance > 5)
# Fit selected models on glb_trnobs_df
for (mdl_id in gsub(".prob", "",
gsub(glb_rsp_var_out, "", row.names(mdlimp_df), fixed=TRUE),
fixed=TRUE)) {
mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
ret_lst <-
myfit_mdl(mdl_id=paste0(c(head(mdl_id_components, -1), "Train"),
collapse="."),
model_method=tail(mdl_id_components, 1),
indep_vars_vctr=trim(unlist(strsplit(
glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]"))),
model_type=glb_model_type,
rsp_var=glb_rsp_var, rsp_var_out=glb_rsp_var_out,
fit_df=glb_trnobs_df, OOB_df=NULL,
n_cv_folds=glb_rcv_n_folds, tune_models_df=glb_tune_models_df)
glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df,
mdl_id=tail(glb_models_df$id, 1),
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=subset(glb_models_df,
mdl_id == mdl_id)$opt.prob.threshold.OOB)
glb_newobs_df <- glb_get_predictions(df=glb_newobs_df,
mdl_id=tail(glb_models_df$id, 1),
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=subset(glb_models_df,
mdl_id == mdl_id)$opt.prob.threshold.OOB)
}
}
# "Final" model
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the mdl_id
model_method <- tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)
if (grepl("Ensemble", glb_sel_mdl_id)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), importance > 5)
if (glb_is_classification && glb_is_binomial)
indep_vars_vctr <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
row.names(mdlimp_df)) else
indep_vars_vctr <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
row.names(mdlimp_df))
} else indep_vars_vctr <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
glb_sel_mdl_id
, "feats"], "[,]")))
# Discontinuing use of tune_finmdl_df;
# since final model needs to be cved on glb_trnobs_df
tune_finmdl_df <- NULL
if (nrow(glb_sel_mdl$bestTune) > 0) {
for (param in names(glb_sel_mdl$bestTune)) {
#print(sprintf("param: %s", param))
if (glb_sel_mdl$bestTune[1, param] != "none")
tune_finmdl_df <- rbind(tune_finmdl_df,
data.frame(parameter=param,
min=glb_sel_mdl$bestTune[1, param],
max=glb_sel_mdl$bestTune[1, param],
by=1)) # by val does not matter
}
}
# Sync with parameters in mydsutils.R
#stop(here"); glb_to_sav(); glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df
if (!is.null(glb_preproc_methods) &&
((match_pos <- regexpr(gsub(".", "\\.", paste(glb_preproc_methods, collapse="|"),
fixed=TRUE), glb_sel_mdl_id)) != -1))
ths_preProcess <- str_sub(glb_sel_mdl_id, match_pos,
match_pos + attr(match_pos, "match.length") - 1) else
ths_preProcess <- NULL
fit_trnobs_df <- if (is.null(glb_obstrn_outliers)) glb_trnobs_df else
glb_trnobs_df[!(glb_trnobs_df[, glb_id_var] %in% glb_obstrn_outliers), ]
# Force fitting of Final.glm to identify outliers
method_vctr <- unique(c("glm", tail(unlist(strsplit(glb_sel_mdl_id, "[.]")), 1)))
for (method in method_vctr) {
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=ifelse(grepl("Ensemble", glb_sel_mdl_id), "Final.Ensemble", "Final"),
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
train.method=method,
train.preProcess=ths_preProcess)),
indep_vars=indep_vars_vctr, rsp_var=glb_rsp_var,
fit_df=fit_trnobs_df, OOB_df=NULL)
if ((length(method_vctr) == 1) || (method != "glm")) {
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glb_fin_mdl_id <- glb_models_df[length(glb_models_lst), "id"]
}
}
}## Warning: Final model same as glb_sel_mdl_idrm(ret_lst)## Warning in rm(ret_lst): object 'ret_lst' not foundglb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)## label step_major step_minor label_minor bgn end
## 14 fit.data.training 8 0 0 466.771 467.204
## 15 fit.data.training 8 1 1 467.205 NA
## elapsed
## 14 0.433
## 15 NA#stop(here"); glb_to_sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glb_fin_mdl_id)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
for (mdl_id in glb_mdl_ensemble) {
glb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=prob_threshold)
glb_newobs_df <- glb_get_predictions(df=glb_newobs_df, mdl_id=mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=prob_threshold)
}
}## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleadingglb_trnobs_df <- glb_get_predictions(df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
rsp_var_out=glb_rsp_var_out,
prob_threshold_def=prob_threshold)
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
glb_featsimp_df[, paste0(glb_fin_mdl_id, ".importance")] <- glb_featsimp_df$importance
print(glb_featsimp_df)## Ensemble.glmnet.importance
## startprice.log10.predict.RFE.X.spatialSign.rf 100.00000
## startprice.log10.predict.RFE.X.rf 39.53565
## startprice.log10.predict.RFE.X.YeoJohnson.rf 31.42341
## startprice.log10.predict.RFE.X.center.scale.rf 24.23239
## startprice.log10.predict.RFE.X.earth 22.94697
## startprice.log10.predict.RFE.X.avNNet 22.37479
## startprice.log10.predict.RFE.X.glm 22.37479
## startprice.log10.predict.RFE.X.glmnet 22.37479
## startprice.log10.predict.RFE.X.svmLinear 22.37331
## startprice.log10.predict.RFE.X.gbm 19.93481
## startprice.log10.predict.RFE.X.bagEarth 18.94024
## startprice.log10.predict.RFE.X.svmLinear2 16.76393
## startprice.log10.predict.RFE.X.nnet 14.50780
## startprice.log10.predict.RFE.X.rpart 0.00000
## importance
## startprice.log10.predict.RFE.X.spatialSign.rf 100.00000
## startprice.log10.predict.RFE.X.rf 39.53565
## startprice.log10.predict.RFE.X.YeoJohnson.rf 31.42341
## startprice.log10.predict.RFE.X.center.scale.rf 24.23239
## startprice.log10.predict.RFE.X.earth 22.94697
## startprice.log10.predict.RFE.X.avNNet 22.37479
## startprice.log10.predict.RFE.X.glm 22.37479
## startprice.log10.predict.RFE.X.glmnet 22.37479
## startprice.log10.predict.RFE.X.svmLinear 22.37331
## startprice.log10.predict.RFE.X.gbm 19.93481
## startprice.log10.predict.RFE.X.bagEarth 18.94024
## startprice.log10.predict.RFE.X.svmLinear2 16.76393
## startprice.log10.predict.RFE.X.nnet 14.50780
## startprice.log10.predict.RFE.X.rpart 0.00000
## Final.Ensemble.glmnet.importance
## startprice.log10.predict.RFE.X.spatialSign.rf 100.00000
## startprice.log10.predict.RFE.X.rf 39.53565
## startprice.log10.predict.RFE.X.YeoJohnson.rf 31.42341
## startprice.log10.predict.RFE.X.center.scale.rf 24.23239
## startprice.log10.predict.RFE.X.earth 22.94697
## startprice.log10.predict.RFE.X.avNNet 22.37479
## startprice.log10.predict.RFE.X.glm 22.37479
## startprice.log10.predict.RFE.X.glmnet 22.37479
## startprice.log10.predict.RFE.X.svmLinear 22.37331
## startprice.log10.predict.RFE.X.gbm 19.93481
## startprice.log10.predict.RFE.X.bagEarth 18.94024
## startprice.log10.predict.RFE.X.svmLinear2 16.76393
## startprice.log10.predict.RFE.X.nnet 14.50780
## startprice.log10.predict.RFE.X.rpart 0.00000if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id,
prob_threshold=glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id) ## Warning in glb_analytics_diag_plots(obs_df = glb_trnobs_df, mdl_id =
## glb_fin_mdl_id): Limiting important feature scatter plots to 5 out of 14
## UniqueID
## 665 11330
## 56 10166
## 464 10781
## 317 10542
## 391 10666
## description
## 665
## 56
## 464 This iPad is in very good condition. Includes charger. Slight scratches on back and small bend near
## 317 Screen is perfect with a few minor scratches on the back (see photos)
## 391 Great Condition, No charger
## startprice condition cellular carrier color storage
## 665 0.01 New Unknown Unknown White 16
## 56 0.01 Seller refurbished 0 None Unknown 32
## 464 0.01 Used 0 None Unknown 32
## 317 0.01 Used 0 None Unknown 16
## 391 0.01 Used 0 None Unknown 16
## productline .src .grpid startprice.log10 .rnorm
## 665 iPad mini 3 Train <NA> -2 1.57737266
## 56 iPad 4 Train <NA> -2 -0.03321145
## 464 iPad 2 Train <NA> -2 0.08466900
## 317 iPad 3 Train <NA> -2 -0.69662752
## 391 iPad 2 Train <NA> -2 1.79828280
## startprice.log10.cut.fctr
## 665 (-2,-0.352]
## 56 (-2,-0.352]
## 464 (-2,-0.352]
## 317 (-2,-0.352]
## 391 (-2,-0.352]
## descr.my
## 665
## 56
## 464 This iPad is in very good condition. Includes charger. Slight scratches on back and small bent near
## 317 Screen is perfect with a few minor scratches on the back (see photos)
## 391 Great Condition, No charger
## condition.fctr cellular.fctr carrier.fctr color.fctr storage.fctr
## 665 New Unknown Unknown White 16
## 56 Seller refurbished 0 None Unknown 32
## 464 Used 0 None Unknown 32
## 317 Used 0 None Unknown 16
## 391 Used 0 None Unknown 16
## D.terms.post.stop.n D.terms.post.stop.n.log D.weight.post.stop.sum
## 665 0 0.000000 0.000000
## 56 0 0.000000 0.000000
## 464 8 2.197225 5.256890
## 317 6 1.945910 5.035368
## 391 4 1.609438 4.384451
## D.terms.post.stem.n D.terms.post.stem.n.log D.weight.post.stem.sum
## 665 0 0.000000 0.000000
## 56 0 0.000000 0.000000
## 464 8 2.197225 5.157963
## 317 6 1.945910 4.903465
## 391 4 1.609438 4.384451
## D.weight.sum.stem.stop.Ratio D.wrds.n.log D.wrds.unq.n.log
## 665 1.0000000 0.000000 0.000000
## 56 1.0000000 0.000000 0.000000
## 464 0.9811815 2.890372 2.197225
## 317 0.9738048 2.639057 1.945910
## 391 1.0000000 1.609438 1.609438
## D.weight.sum D.ratio.weight.sum.wrds.n D.chrs.n.log D.chrs.uppr.n.log
## 665 0.000000 0.0000000 0.000000 0.000000
## 56 0.000000 0.0000000 0.000000 0.000000
## 464 5.157963 0.3034096 4.615121 4.406719
## 317 4.903465 0.3771896 4.248495 4.025352
## 391 4.384451 1.0961128 3.332205 3.178054
## D.chrs.pnct11.n.log D.chrs.pnct13.n.log D.wrds.stop.n.log
## 665 0.0000000 0.000000 0.000000
## 56 0.0000000 0.000000 0.000000
## 464 0.0000000 1.098612 2.197225
## 317 0.0000000 0.000000 2.079442
## 391 0.6931472 0.000000 0.000000
## D.ratio.wrds.stop.n.wrds.n prdl.descr.my.fctr startprice.dgt1.is9
## 665 1.0000000 iPadmini3#0 0
## 56 1.0000000 iPad4#0 0
## 464 0.5000000 iPad2#1 0
## 317 0.5714286 iPad3#1 0
## 391 0.2000000 iPad2#1 0
## startprice.dgt2.is9 startprice.dcm1.is9 startprice.dcm2.is9 .clusterid
## 665 0 0 0 1
## 56 0 0 0 1
## 464 0 0 0 2
## 317 0 0 0 2
## 391 0 0 0 1
## .clusterid.fctr .lcn sold biddable D.terms.n.stem.stop.Ratio
## 665 1 OOB 1 NA NA
## 56 1 OOB 1 NA NA
## 464 2 OOB 1 NA NA
## 317 2 OOB 1 NA NA
## 391 1 OOB 1 NA NA
## D.T.condit D.T.use D.T.new D.T.screen D.T.scratch D.T.good D.T.great
## 665 NA NA NA NA NA NA NA
## 56 NA NA NA NA NA NA NA
## 464 NA NA NA NA NA NA NA
## 317 NA NA NA NA NA NA NA
## 391 NA NA NA NA NA NA NA
## D.T.excel D.T.work D.T.no D.T.function. D.T.like D.T.crack D.T.fulli
## 665 NA NA NA NA NA NA NA
## 56 NA NA NA NA NA NA NA
## 464 NA NA NA NA NA NA NA
## 317 NA NA NA NA NA NA NA
## 391 NA NA NA NA NA NA NA
## D.T.box D.T.mint D.T.item D.T.lock D.T.see D.T.icloud D.T.perfect
## 665 NA NA NA NA NA NA NA
## 56 NA NA NA NA NA NA NA
## 464 NA NA NA NA NA NA NA
## 317 NA NA NA NA NA NA NA
## 391 NA NA NA NA NA NA NA
## D.dgts.n.log D.chrs.pnct01.n.log D.chrs.pnct02.n.log
## 665 NA NA NA
## 56 NA NA NA
## 464 NA NA NA
## 317 NA NA NA
## 391 NA NA NA
## D.chrs.pnct03.n.log D.chrs.pnct04.n.log D.chrs.pnct05.n.log
## 665 NA NA NA
## 56 NA NA NA
## 464 NA NA NA
## 317 NA NA NA
## 391 NA NA NA
## D.chrs.pnct06.n.log D.chrs.pnct07.n.log D.chrs.pnct08.n.log
## 665 NA NA NA
## 56 NA NA NA
## 464 NA NA NA
## 317 NA NA NA
## 391 NA NA NA
## D.chrs.pnct09.n.log D.chrs.pnct10.n.log D.chrs.pnct12.n.log
## 665 NA NA NA
## 56 NA NA NA
## 464 NA NA NA
## 317 NA NA NA
## 391 NA NA NA
## D.chrs.pnct14.n.log D.chrs.pnct15.n.log D.chrs.pnct16.n.log
## 665 NA NA NA
## 56 NA NA NA
## 464 NA NA NA
## 317 NA NA NA
## 391 NA NA NA
## D.chrs.pnct17.n.log D.chrs.pnct18.n.log D.chrs.pnct19.n.log
## 665 NA NA NA
## 56 NA NA NA
## 464 NA NA NA
## 317 NA NA NA
## 391 NA NA NA
## D.chrs.pnct20.n.log D.chrs.pnct21.n.log D.chrs.pnct22.n.log
## 665 NA NA NA
## 56 NA NA NA
## 464 NA NA NA
## 317 NA NA NA
## 391 NA NA NA
## D.chrs.pnct23.n.log D.chrs.pnct24.n.log D.chrs.pnct25.n.log
## 665 NA NA NA
## 56 NA NA NA
## 464 NA NA NA
## 317 NA NA NA
## 391 NA NA NA
## D.chrs.pnct26.n.log D.chrs.pnct27.n.log D.chrs.pnct28.n.log
## 665 NA NA NA
## 56 NA NA NA
## 464 NA NA NA
## 317 NA NA NA
## 391 NA NA NA
## D.chrs.pnct29.n.log D.chrs.pnct30.n.log D.P.http D.P.mini D.P.air
## 665 NA NA NA NA NA
## 56 NA NA NA NA NA
## 464 NA NA NA NA NA
## 317 NA NA NA NA NA
## 391 NA NA NA NA NA
## D.P.black D.P.white D.P.gold D.P.spacegray startprice.dgt3.is9
## 665 NA NA NA NA NA
## 56 NA NA NA NA NA
## 464 NA NA NA NA NA
## 317 NA NA NA NA NA
## 391 NA NA NA NA NA
## startprice.log10.predict.RFE.X.spatialSign.rf
## 665 2.337335
## 56 1.956806
## 464 1.964634
## 317 1.944459
## 391 1.806030
## startprice.log10.predict.RFE.X.spatialSign.rf.err
## 665 4.337335
## 56 3.956806
## 464 3.964634
## 317 3.944459
## 391 3.806030
## startprice.log10.predict.RFE.X.spatialSign.rf.err.abs
## 665 4.337335
## 56 3.956806
## 464 3.964634
## 317 3.944459
## 391 3.806030
## startprice.log10.predict.RFE.X.spatialSign.rf.accurate
## 665 FALSE
## 56 FALSE
## 464 FALSE
## 317 FALSE
## 391 FALSE
## startprice.log10.predict.RFE.X.YeoJohnson.rf
## 665 2.070178
## 56 2.016833
## 464 1.962008
## 317 1.857597
## 391 1.846653
## startprice.log10.predict.RFE.X.YeoJohnson.rf.err
## 665 4.070178
## 56 4.016833
## 464 3.962008
## 317 3.857597
## 391 3.846653
## startprice.log10.predict.RFE.X.YeoJohnson.rf.err.abs
## 665 4.070178
## 56 4.016833
## 464 3.962008
## 317 3.857597
## 391 3.846653
## startprice.log10.predict.RFE.X.YeoJohnson.rf.accurate
## 665 FALSE
## 56 FALSE
## 464 FALSE
## 317 FALSE
## 391 FALSE
## startprice.log10.predict.RFE.X.center.scale.rf
## 665 2.070178
## 56 2.016833
## 464 1.963026
## 317 1.857655
## 391 1.846104
## startprice.log10.predict.RFE.X.center.scale.rf.err
## 665 4.070178
## 56 4.016833
## 464 3.963026
## 317 3.857655
## 391 3.846104
## startprice.log10.predict.RFE.X.center.scale.rf.err.abs
## 665 4.070178
## 56 4.016833
## 464 3.963026
## 317 3.857655
## 391 3.846104
## startprice.log10.predict.RFE.X.center.scale.rf.accurate
## 665 FALSE
## 56 FALSE
## 464 FALSE
## 317 FALSE
## 391 FALSE
## startprice.log10.predict.RFE.X.rf
## 665 2.070178
## 56 2.016833
## 464 1.963026
## 317 1.857655
## 391 1.846104
## startprice.log10.predict.RFE.X.rf.err
## 665 4.070178
## 56 4.016833
## 464 3.963026
## 317 3.857655
## 391 3.846104
## startprice.log10.predict.RFE.X.rf.err.abs
## 665 4.070178
## 56 4.016833
## 464 3.963026
## 317 3.857655
## 391 3.846104
## startprice.log10.predict.RFE.X.rf.accurate
## 665 FALSE
## 56 FALSE
## 464 FALSE
## 317 FALSE
## 391 FALSE
## startprice.log10.predict.RFE.X.avNNet
## 665 0.9990158
## 56 0.9987748
## 464 0.9995232
## 317 0.9985766
## 391 0.9979419
## startprice.log10.predict.RFE.X.avNNet.err
## 665 2.999016
## 56 2.998775
## 464 2.999523
## 317 2.998577
## 391 2.997942
## startprice.log10.predict.RFE.X.avNNet.err.abs
## 665 2.999016
## 56 2.998775
## 464 2.999523
## 317 2.998577
## 391 2.997942
## startprice.log10.predict.RFE.X.avNNet.accurate
## 665 FALSE
## 56 FALSE
## 464 FALSE
## 317 FALSE
## 391 FALSE
## startprice.log10.predict.RFE.X.bagEarth
## 665 1.956904
## 56 1.964666
## 464 1.937739
## 317 1.952443
## 391 1.912460
## startprice.log10.predict.RFE.X.bagEarth.err
## 665 3.956904
## 56 3.964666
## 464 3.937739
## 317 3.952443
## 391 3.912460
## startprice.log10.predict.RFE.X.bagEarth.err.abs
## 665 3.956904
## 56 3.964666
## 464 3.937739
## 317 3.952443
## 391 3.912460
## startprice.log10.predict.RFE.X.bagEarth.accurate
## 665 FALSE
## 56 FALSE
## 464 FALSE
## 317 FALSE
## 391 FALSE
## startprice.log10.predict.RFE.X.earth
## 665 1.779266
## 56 1.779266
## 464 1.779266
## 317 1.779266
## 391 1.779266
## startprice.log10.predict.RFE.X.earth.err
## 665 3.779266
## 56 3.779266
## 464 3.779266
## 317 3.779266
## 391 3.779266
## startprice.log10.predict.RFE.X.earth.err.abs
## 665 3.779266
## 56 3.779266
## 464 3.779266
## 317 3.779266
## 391 3.779266
## startprice.log10.predict.RFE.X.earth.accurate
## 665 FALSE
## 56 FALSE
## 464 FALSE
## 317 FALSE
## 391 FALSE
## startprice.log10.predict.RFE.X.gbm
## 665 1.917832
## 56 2.002162
## 464 2.020197
## 317 2.015259
## 391 1.857118
## startprice.log10.predict.RFE.X.gbm.err
## 665 3.917832
## 56 4.002162
## 464 4.020197
## 317 4.015259
## 391 3.857118
## startprice.log10.predict.RFE.X.gbm.err.abs
## 665 3.917832
## 56 4.002162
## 464 4.020197
## 317 4.015259
## 391 3.857118
## startprice.log10.predict.RFE.X.gbm.accurate
## 665 FALSE
## 56 FALSE
## 464 FALSE
## 317 FALSE
## 391 FALSE
## startprice.log10.predict.RFE.X.glmnet
## 665 1.886334
## 56 1.644223
## 464 1.685963
## 317 1.663295
## 391 1.513616
## startprice.log10.predict.RFE.X.glmnet.err
## 665 3.886334
## 56 3.644223
## 464 3.685963
## 317 3.663295
## 391 3.513616
## startprice.log10.predict.RFE.X.glmnet.err.abs
## 665 3.886334
## 56 3.644223
## 464 3.685963
## 317 3.663295
## 391 3.513616
## startprice.log10.predict.RFE.X.glmnet.accurate
## 665 FALSE
## 56 FALSE
## 464 FALSE
## 317 FALSE
## 391 FALSE
## startprice.log10.predict.RFE.X.nnet
## 665 0.9988585
## 56 0.9988754
## 464 0.9994913
## 317 0.9984435
## 391 0.9979955
## startprice.log10.predict.RFE.X.nnet.err
## 665 2.998858
## 56 2.998875
## 464 2.999491
## 317 2.998444
## 391 2.997995
## startprice.log10.predict.RFE.X.nnet.err.abs
## 665 2.998858
## 56 2.998875
## 464 2.999491
## 317 2.998444
## 391 2.997995
## startprice.log10.predict.RFE.X.nnet.accurate
## 665 FALSE
## 56 FALSE
## 464 FALSE
## 317 FALSE
## 391 FALSE
## startprice.log10.predict.RFE.X.svmLinear2
## 665 1.928004
## 56 1.800848
## 464 1.837870
## 317 1.833991
## 391 1.797528
## startprice.log10.predict.RFE.X.svmLinear2.err
## 665 3.928004
## 56 3.800848
## 464 3.837870
## 317 3.833991
## 391 3.797528
## startprice.log10.predict.RFE.X.svmLinear2.err.abs
## 665 3.928004
## 56 3.800848
## 464 3.837870
## 317 3.833991
## 391 3.797528
## startprice.log10.predict.RFE.X.svmLinear2.accurate
## 665 FALSE
## 56 FALSE
## 464 FALSE
## 317 FALSE
## 391 FALSE
## startprice.log10.predict.RFE.X.glm
## 665 1.824446
## 56 1.678870
## 464 1.806622
## 317 1.704068
## 391 1.554044
## startprice.log10.predict.RFE.X.glm.err
## 665 3.824446
## 56 3.678870
## 464 3.806622
## 317 3.704068
## 391 3.554044
## startprice.log10.predict.RFE.X.glm.err.abs
## 665 3.824446
## 56 3.678870
## 464 3.806622
## 317 3.704068
## 391 3.554044
## startprice.log10.predict.RFE.X.glm.accurate
## 665 FALSE
## 56 FALSE
## 464 FALSE
## 317 FALSE
## 391 FALSE
## startprice.log10.predict.RFE.X.svmLinear
## 665 1.963542
## 56 1.810945
## 464 1.844632
## 317 1.839785
## 391 1.793669
## startprice.log10.predict.RFE.X.svmLinear.err
## 665 3.963542
## 56 3.810945
## 464 3.844632
## 317 3.839785
## 391 3.793669
## startprice.log10.predict.RFE.X.svmLinear.err.abs
## 665 3.963542
## 56 3.810945
## 464 3.844632
## 317 3.839785
## 391 3.793669
## startprice.log10.predict.RFE.X.svmLinear.accurate
## 665 FALSE
## 56 FALSE
## 464 FALSE
## 317 FALSE
## 391 FALSE
## startprice.log10.predict.RFE.X.rpart
## 665 2.223889
## 56 1.955542
## 464 1.955542
## 317 1.955542
## 391 1.955542
## startprice.log10.predict.RFE.X.rpart.err
## 665 4.223889
## 56 3.955542
## 464 3.955542
## 317 3.955542
## 391 3.955542
## startprice.log10.predict.RFE.X.rpart.err.abs
## 665 4.223889
## 56 3.955542
## 464 3.955542
## 317 3.955542
## 391 3.955542
## startprice.log10.predict.RFE.X.rpart.accurate
## 665 FALSE
## 56 FALSE
## 464 FALSE
## 317 FALSE
## 391 FALSE
## startprice.log10.predict.Ensemble.glmnet
## 665 NA
## 56 NA
## 464 NA
## 317 NA
## 391 NA
## startprice.log10.predict.Ensemble.glmnet.err
## 665 NA
## 56 NA
## 464 NA
## 317 NA
## 391 NA
## startprice.log10.predict.Ensemble.glmnet.err.abs
## 665 NA
## 56 NA
## 464 NA
## 317 NA
## 391 NA
## startprice.log10.predict.Ensemble.glmnet.accurate
## 665 NA
## 56 NA
## 464 NA
## 317 NA
## 391 NA
## startprice.log10.predict.Final.Ensemble.glmnet
## 665 2.375689
## 56 2.008512
## 464 1.992422
## 317 1.924771
## 391 1.770233
## startprice.log10.predict.Final.Ensemble.glmnet.err
## 665 4.375689
## 56 4.008512
## 464 3.992422
## 317 3.924771
## 391 3.770233
## startprice.log10.predict.Final.Ensemble.glmnet.err.abs
## 665 4.375689
## 56 4.008512
## 464 3.992422
## 317 3.924771
## 391 3.770233
## startprice.log10.predict.Final.Ensemble.glmnet.accurate .label
## 665 FALSE 11330
## 56 FALSE 10166
## 464 FALSE 10781
## 317 FALSE 10542
## 391 FALSE 10666
dsp_feats_vctr <- c(NULL)
for(var in grep(".importance", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glb_trnobs_df[glb_trnobs_df$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glb_trnobs_df), value=TRUE)])
print(setdiff(names(glb_trnobs_df), names(glb_allobs_df)))## [1] "startprice.log10.predict.Final.Ensemble.glmnet"
## [2] "startprice.log10.predict.Final.Ensemble.glmnet.err"
## [3] "startprice.log10.predict.Final.Ensemble.glmnet.err.abs"
## [4] "startprice.log10.predict.Final.Ensemble.glmnet.accurate"for (col in setdiff(names(glb_trnobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.src == "Train", col] <- glb_trnobs_df[, col]
print(setdiff(names(glb_fitobs_df), names(glb_allobs_df)))## character(0)print(setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))## character(0)for (col in setdiff(names(glb_OOBobs_df), names(glb_allobs_df)))
# Merge or cbind ?
glb_allobs_df[glb_allobs_df$.lcn == "OOB", col] <- glb_OOBobs_df[, col]
print(setdiff(names(glb_newobs_df), names(glb_allobs_df)))## character(0)if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "dsk.RData"))
replay.petrisim(pn=glb_analytics_pn,
replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord=TRUE)## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: data.training.all
## 1.0000 1 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.new
## 2.0000 2 1 1 1 0
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction data.new.prediction firing: model.selected
## 3.0000 3 0 2 1 0
## 3.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: data.training.all.prediction
## 4.0000 5 0 1 1 1
## 4.0000 multiple enabled transitions: model.final data.training.all.prediction data.new.prediction firing: model.final
## 5.0000 4 0 0 2 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 15 fit.data.training 8 1 1 467.205 475.545
## 16 predict.data.new 9 0 0 475.546 NA
## elapsed
## 15 8.34
## 16 NAStep 9.0: predict data new
# Compute final model predictions
#glb_to_sav(); all.equal(sav_allobs_df, glb_allobs_df); all.equal(sav_trnobs_df, glb_trnobs_df); all.equal(sav_newobs_df, glb_newobs_df)
if (glb_is_classification && glb_is_binomial)
prob_threshold_def <-
glb_models_df[glb_models_df$id == glb_sel_mdl_id, "opt.prob.threshold.OOB"] else
prob_threshold_def <- NULL
for (obsSet in c("trn", "new")) {
obs_df <- switch(obsSet, all = glb_allobs_df, trn = glb_trnobs_df, new = glb_newobs_df)
obs_df <- glb_get_predictions(obs_df, mdl_id = glb_fin_mdl_id,
rsp_var_out = glb_rsp_var_out, prob_threshold_def = prob_threshold_def)
if (obsSet == "all") glb_allobs_df <- obs_df else
if (obsSet == "trn") glb_trnobs_df <- obs_df else
if (obsSet == "new") glb_newobs_df <- obs_df
}
rm(obs_df)
glb_allobs_df <- orderBy(reformulate(glb_id_var), myrbind_df(glb_trnobs_df, glb_newobs_df))
glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id = glb_fin_mdl_id,
prob_threshold = prob_threshold_def)## Warning in glb_analytics_diag_plots(obs_df = glb_newobs_df, mdl_id =
## glb_fin_mdl_id, : Limiting important feature scatter plots to 5 out of 14
## UniqueID
## 883 11974
## 1051 12215
## 966 12084
## 842 11891
## 948 12061
## description
## 883
## 1051 The iPad is in great shape, having been gently used and covered since purchase. The Speck cover is
## 966 Minor wear and tear. Device is fully operational and in working order. No major scratches on screen
## 842
## 948
## startprice condition cellular carrier color storage
## 883 0.01 New 0 None Unknown 16
## 1051 0.01 Used 0 None Black 16
## 966 0.01 Used 0 None White 16
## 842 0.01 Seller refurbished 0 None Unknown 64
## 948 0.01 Used 1 Unknown White 32
## productline .src .grpid startprice.log10 .rnorm
## 883 iPad Air 2 Test <NA> -2 0.2019409
## 1051 iPad mini Test <NA> -2 -0.6645831
## 966 iPad 2 Test <NA> -2 0.4274970
## 842 iPad 4 Test <NA> -2 3.6361055
## 948 iPad 1 Test <NA> -2 -0.9854898
## startprice.log10.cut.fctr
## 883 (-2,-0.352]
## 1051 (-2,-0.352]
## 966 (-2,-0.352]
## 842 (-2,-0.352]
## 948 (-2,-0.352]
## descr.my
## 883
## 1051 The iPad is in great shape, having been gently used and covered since purchase. The Speck cover is
## 966 Minor wear and tear. Device is fully operational and in working order. No major scratches on screen
## 842
## 948
## condition.fctr cellular.fctr carrier.fctr color.fctr storage.fctr
## 883 New 0 None Unknown 16
## 1051 Used 0 None Black 16
## 966 Used 0 None White 16
## 842 Seller refurbished 0 None Unknown 64
## 948 Used 1 Unknown White 32
## D.terms.post.stop.n D.terms.post.stop.n.log D.weight.post.stop.sum
## 883 0 0.000000 0.000000
## 1051 5 1.791759 6.438998
## 966 11 2.484907 5.577275
## 842 0 0.000000 0.000000
## 948 0 0.000000 0.000000
## D.terms.post.stem.n D.terms.post.stem.n.log D.weight.post.stem.sum
## 883 0 0.000000 0.000000
## 1051 5 1.791759 6.390390
## 966 11 2.484907 5.469383
## 842 0 0.000000 0.000000
## 948 0 0.000000 0.000000
## D.weight.sum.stem.stop.Ratio D.wrds.n.log D.wrds.unq.n.log
## 883 1.0000000 0.000000 0.000000
## 1051 0.9924509 2.944439 1.791759
## 966 0.9806549 2.890372 2.484907
## 842 1.0000000 0.000000 0.000000
## 948 1.0000000 0.000000 0.000000
## D.weight.sum D.ratio.weight.sum.wrds.n D.chrs.n.log D.chrs.uppr.n.log
## 883 0.000000 0.0000000 0.000000 0.000000
## 1051 6.390390 0.3550216 4.615121 4.382027
## 966 5.469383 0.3217284 4.615121 4.406719
## 842 0.000000 0.0000000 0.000000 0.000000
## 948 0.000000 0.0000000 0.000000 0.000000
## D.chrs.pnct11.n.log D.chrs.pnct13.n.log D.wrds.stop.n.log
## 883 0.0000000 0.0000000 0.000000
## 1051 0.6931472 0.6931472 2.564949
## 966 0.0000000 1.0986123 1.945910
## 842 0.0000000 0.0000000 0.000000
## 948 0.0000000 0.0000000 0.000000
## D.ratio.wrds.stop.n.wrds.n prdl.descr.my.fctr startprice.dgt1.is9
## 883 1.0000000 iPadAir2#0 0
## 1051 0.6842105 iPadmini#1 0
## 966 0.3888889 iPad2#1 0
## 842 1.0000000 iPad4#0 0
## 948 1.0000000 iPad1#0 0
## startprice.dgt2.is9 startprice.dcm1.is9 startprice.dcm2.is9
## 883 0 0 0
## 1051 0 0 0
## 966 0 0 0
## 842 0 0 0
## 948 0 0 0
## .clusterid .clusterid.fctr .lcn sold
## 883 1 1 OOB NA
## 1051 2 2 OOB NA
## 966 3 3 OOB NA
## 842 1 1 OOB NA
## 948 1 1 OOB NA
## startprice.log10.predict.RFE.X.spatialSign.rf
## 883 2.384565
## 1051 2.033334
## 966 1.986233
## 842 1.956806
## 948 1.782399
## startprice.log10.predict.RFE.X.spatialSign.rf.err
## 883 4.384565
## 1051 4.033334
## 966 3.986233
## 842 3.956806
## 948 3.782399
## startprice.log10.predict.RFE.X.spatialSign.rf.err.abs
## 883 4.384565
## 1051 4.033334
## 966 3.986233
## 842 3.956806
## 948 3.782399
## startprice.log10.predict.RFE.X.spatialSign.rf.accurate
## 883 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## 948 FALSE
## startprice.log10.predict.RFE.X.YeoJohnson.rf
## 883 2.385705
## 1051 1.929887
## 966 2.006112
## 842 2.016833
## 948 1.841376
## startprice.log10.predict.RFE.X.YeoJohnson.rf.err
## 883 4.385705
## 1051 3.929887
## 966 4.006112
## 842 4.016833
## 948 3.841376
## startprice.log10.predict.RFE.X.YeoJohnson.rf.err.abs
## 883 4.385705
## 1051 3.929887
## 966 4.006112
## 842 4.016833
## 948 3.841376
## startprice.log10.predict.RFE.X.YeoJohnson.rf.accurate
## 883 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## 948 FALSE
## startprice.log10.predict.RFE.X.center.scale.rf
## 883 2.385705
## 1051 1.929589
## 966 2.005458
## 842 2.016833
## 948 1.841376
## startprice.log10.predict.RFE.X.center.scale.rf.err
## 883 4.385705
## 1051 3.929589
## 966 4.005458
## 842 4.016833
## 948 3.841376
## startprice.log10.predict.RFE.X.center.scale.rf.err.abs
## 883 4.385705
## 1051 3.929589
## 966 4.005458
## 842 4.016833
## 948 3.841376
## startprice.log10.predict.RFE.X.center.scale.rf.accurate
## 883 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## 948 FALSE
## startprice.log10.predict.RFE.X.rf
## 883 2.385705
## 1051 1.929589
## 966 2.005458
## 842 2.016833
## 948 1.841376
## startprice.log10.predict.RFE.X.rf.err
## 883 4.385705
## 1051 3.929589
## 966 4.005458
## 842 4.016833
## 948 3.841376
## startprice.log10.predict.RFE.X.rf.err.abs
## 883 4.385705
## 1051 3.929589
## 966 4.005458
## 842 4.016833
## 948 3.841376
## startprice.log10.predict.RFE.X.rf.accurate
## 883 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## 948 FALSE
## startprice.log10.predict.RFE.X.avNNet
## 883 0.9988713
## 1051 0.9993395
## 966 0.9996110
## 842 0.9987748
## 948 0.9994895
## startprice.log10.predict.RFE.X.avNNet.err
## 883 2.998871
## 1051 2.999339
## 966 2.999611
## 842 2.998775
## 948 2.999490
## startprice.log10.predict.RFE.X.avNNet.err.abs
## 883 2.998871
## 1051 2.999339
## 966 2.999611
## 842 2.998775
## 948 2.999490
## startprice.log10.predict.RFE.X.avNNet.accurate
## 883 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## 948 FALSE
## startprice.log10.predict.RFE.X.bagEarth
## 883 2.335926
## 1051 1.931170
## 966 2.012768
## 842 1.964666
## 948 1.876600
## startprice.log10.predict.RFE.X.bagEarth.err
## 883 4.335926
## 1051 3.931170
## 966 4.012768
## 842 3.964666
## 948 3.876600
## startprice.log10.predict.RFE.X.bagEarth.err.abs
## 883 4.335926
## 1051 3.931170
## 966 4.012768
## 842 3.964666
## 948 3.876600
## startprice.log10.predict.RFE.X.bagEarth.accurate
## 883 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## 948 FALSE
## startprice.log10.predict.RFE.X.earth
## 883 2.200465
## 1051 1.779266
## 966 1.779266
## 842 1.779266
## 948 1.779266
## startprice.log10.predict.RFE.X.earth.err
## 883 4.200465
## 1051 3.779266
## 966 3.779266
## 842 3.779266
## 948 3.779266
## startprice.log10.predict.RFE.X.earth.err.abs
## 883 4.200465
## 1051 3.779266
## 966 3.779266
## 842 3.779266
## 948 3.779266
## startprice.log10.predict.RFE.X.earth.accurate
## 883 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## 948 FALSE
## startprice.log10.predict.RFE.X.gbm
## 883 2.264936
## 1051 2.028287
## 966 2.035972
## 842 2.002162
## 948 2.001873
## startprice.log10.predict.RFE.X.gbm.err
## 883 4.264936
## 1051 4.028287
## 966 4.035972
## 842 4.002162
## 948 4.001873
## startprice.log10.predict.RFE.X.gbm.err.abs
## 883 4.264936
## 1051 4.028287
## 966 4.035972
## 842 4.002162
## 948 4.001873
## startprice.log10.predict.RFE.X.gbm.accurate
## 883 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## 948 FALSE
## startprice.log10.predict.RFE.X.glmnet
## 883 2.087784
## 1051 1.759549
## 966 1.773414
## 842 1.644223
## 948 1.738736
## startprice.log10.predict.RFE.X.glmnet.err
## 883 4.087784
## 1051 3.759549
## 966 3.773414
## 842 3.644223
## 948 3.738736
## startprice.log10.predict.RFE.X.glmnet.err.abs
## 883 4.087784
## 1051 3.759549
## 966 3.773414
## 842 3.644223
## 948 3.738736
## startprice.log10.predict.RFE.X.glmnet.accurate
## 883 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## 948 FALSE
## startprice.log10.predict.RFE.X.nnet
## 883 0.9986147
## 1051 0.9987734
## 966 0.9994974
## 842 0.9988754
## 948 0.9995780
## startprice.log10.predict.RFE.X.nnet.err
## 883 2.998615
## 1051 2.998773
## 966 2.999497
## 842 2.998875
## 948 2.999578
## startprice.log10.predict.RFE.X.nnet.err.abs
## 883 2.998615
## 1051 2.998773
## 966 2.999497
## 842 2.998875
## 948 2.999578
## startprice.log10.predict.RFE.X.nnet.accurate
## 883 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## 948 FALSE
## startprice.log10.predict.RFE.X.svmLinear2
## 883 2.401182
## 1051 1.869113
## 966 1.932000
## 842 1.800848
## 948 1.805054
## startprice.log10.predict.RFE.X.svmLinear2.err
## 883 4.401182
## 1051 3.869113
## 966 3.932000
## 842 3.800848
## 948 3.805054
## startprice.log10.predict.RFE.X.svmLinear2.err.abs
## 883 4.401182
## 1051 3.869113
## 966 3.932000
## 842 3.800848
## 948 3.805054
## startprice.log10.predict.RFE.X.svmLinear2.accurate
## 883 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## 948 FALSE
## startprice.log10.predict.RFE.X.glm
## 883 2.228872
## 1051 1.822275
## 966 1.936295
## 842 1.678870
## 948 1.776838
## startprice.log10.predict.RFE.X.glm.err
## 883 4.228872
## 1051 3.822275
## 966 3.936295
## 842 3.678870
## 948 3.776838
## startprice.log10.predict.RFE.X.glm.err.abs
## 883 4.228872
## 1051 3.822275
## 966 3.936295
## 842 3.678870
## 948 3.776838
## startprice.log10.predict.RFE.X.glm.accurate
## 883 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## 948 FALSE
## startprice.log10.predict.RFE.X.svmLinear
## 883 2.398344
## 1051 1.870308
## 966 1.940720
## 842 1.810945
## 948 1.806953
## startprice.log10.predict.RFE.X.svmLinear.err
## 883 4.398344
## 1051 3.870308
## 966 3.940720
## 842 3.810945
## 948 3.806953
## startprice.log10.predict.RFE.X.svmLinear.err.abs
## 883 4.398344
## 1051 3.870308
## 966 3.940720
## 842 3.810945
## 948 3.806953
## startprice.log10.predict.RFE.X.svmLinear.accurate
## 883 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## 948 FALSE
## startprice.log10.predict.RFE.X.rpart
## 883 2.311259
## 1051 1.955542
## 966 1.955542
## 842 1.955542
## 948 1.689359
## startprice.log10.predict.RFE.X.rpart.err
## 883 4.311259
## 1051 3.955542
## 966 3.955542
## 842 3.955542
## 948 3.689359
## startprice.log10.predict.RFE.X.rpart.err.abs
## 883 4.311259
## 1051 3.955542
## 966 3.955542
## 842 3.955542
## 948 3.689359
## startprice.log10.predict.RFE.X.rpart.accurate
## 883 FALSE
## 1051 FALSE
## 966 FALSE
## 842 FALSE
## 948 FALSE
## startprice.log10.predict.Final.Ensemble.glmnet
## 883 2.465512
## 1051 2.055826
## 966 2.023203
## 842 2.008512
## 948 1.825556
## startprice.log10.predict.Final.Ensemble.glmnet.err
## 883 4.465512
## 1051 4.055826
## 966 4.023203
## 842 4.008512
## 948 3.825556
## startprice.log10.predict.Final.Ensemble.glmnet.err.abs
## 883 4.465512
## 1051 4.055826
## 966 4.023203
## 842 4.008512
## 948 3.825556
## startprice.log10.predict.Final.Ensemble.glmnet.accurate .label
## 883 FALSE 11974
## 1051 FALSE 12215
## 966 FALSE 12084
## 842 FALSE 11891
## 948 FALSE 12061
if (is.null(glb_out_obs)) obs_df <- glb_newobs_df else
obs_df <- switch(glb_out_obs,
all = glb_allobs_df, trn = glb_trnobs_df, new = glb_newobs_df)
if (glb_is_classification && glb_is_binomial) {
submit_df <- glb_newobs_df[, c(glb_id_var,
paste0(glb_rsp_var_out, glb_fin_mdl_id, ".prob"))]
names(submit_df)[2] <- "Probability1"
# submit_df <- glb_newobs_df[, c(paste0(glb_rsp_var_out, glb_fin_mdl_id)), FALSE]
# names(submit_df)[1] <- "BDscience"
# submit_df$BDscience <- as.numeric(submit_df$BDscience) - 1
# #submit_df <-rbind(submit_df, data.frame(bdanalytics=c(" ")))
# print("Submission Stats:")
# print(table(submit_df$BDscience, useNA = "ifany"))
glb_force_prediction_lst <- list()
glb_force_prediction_lst[["0"]] <- c(11885, 11907, 11932, 11943,
12050, 12115, 12171,
12253, 12285, 12367, 12388, 12399,
12585)
for (obs_id in glb_force_prediction_lst[["0"]]) {
if (is.na(glb_allobs_df[glb_allobs_df[, glb_id_var] == obs_id, ".grpid"]))
stop(".grpid is NA")
# submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
# max(0, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] - 0.5)
}
glb_force_prediction_lst[["1"]] <- c(11871, 11875, 11886,
11913, 11931, 11937, 11967, 11982, 11990, 11991, 11994, 11999,
12000, 12002, 12004, 12018, 12021, 12065, 12072,
12111, 12114, 12126, 12134, 12152, 12172,
12213, 12214, 12233, 12265, 12278, 12299,
12446, 12491,
12505, 12576, 12608, 12630)
for (obs_id in glb_force_prediction_lst[["1"]]) {
if (is.na(glb_allobs_df[glb_allobs_df[, glb_id_var] == obs_id, ".grpid"]))
stop(".grpid is NA")
# submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
# min(0.9999, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] + 0.5)
}
rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
for (obs_id in glb_newobs_df[!is.na(glb_newobs_df[, rsp_var_out]) &
(glb_newobs_df[, rsp_var_out] == "Y") &
(glb_newobs_df[ , "startprice"] > 675), "UniqueID"]) {
# submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] <-
# max(0, submit_df[submit_df[, glb_id_var] == obs_id, "Probability1"] - 0.5)
}
} else {
# submit_df <- glb_newobs_df[, c(glb_id_var,
# paste0(glb_rsp_var_out, glb_fin_mdl_id))]
obsout_df <- obs_df[, glb_id_var, FALSE]
for (clmn in names(glb_out_vars_lst))
if (!grepl("^%<d-%", glb_out_vars_lst[[clmn]]))
obsout_df[, clmn] <- obs_df[, glb_out_vars_lst[[clmn]]] else {
feat <- str_trim(unlist(strsplit(glb_out_vars_lst[[clmn]], "%<d-%"))[2])
obsout_df[, clmn] <- obs_df[, eval(parse(text=feat))]
}
}
if (glb_is_classification) {
rsp_var_out <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
tmp_newobs_df <- subset(glb_newobs_df[, c(glb_id_var, ".grpid", rsp_var_out)],
!is.na(.grpid))
tmp_newobs_df <- merge(tmp_newobs_df, dupgrps_df, by=".grpid", all.x=TRUE)
tmp_newobs_df <- merge(tmp_newobs_df, submit_df, by=glb_id_var, all.x = TRUE)
tmp_newobs_df$.err <-
((tmp_newobs_df$Probability1 > 0.5) & (tmp_newobs_df$sold.0 > 0) |
(tmp_newobs_df$Probability1 < 0.5) & (tmp_newobs_df$sold.1 > 0))
tmp_newobs_df <- orderBy(~UniqueID, subset(tmp_newobs_df, .err == TRUE))
print("Prediction errors in duplicates:")
print(tmp_newobs_df)
# if (nrow(tmp_newobs_df) > 0)
# stop("check Prediction errors in duplicates")
#print(dupobs_df[dupobs_df$.grpid == 26, ])
tmp_newobs_df <- cbind(glb_newobs_df, submit_df[, "Probability1", FALSE])
# if (max(glb_newobs_df[!is.na(glb_newobs_df[, rsp_var_out]) &
# (tmp_newobs_df[, "Probability1"] >= 0.5), "startprice"]) >
# max(glb_allobs_df[!is.na(glb_allobs_df[, glb_rsp_var]) &
# (glb_allobs_df[, glb_rsp_var] == "Y"), "startprice"]))
# stop("startprice for some +ve predictions > 675")
# Check predictions that are outside of data ranges
#stop(here")
tmp_feats_df <- subset(glb_feats_df,
!nzv &
(exclude.as.feat != 1) &
!grepl(".fctr", id, fixed=TRUE))[, "id", FALSE]
ranges_all_df <- glb_allobs_df[, tmp_feats_df$id] %>%
dplyr::summarise_each(funs(min(., na.rm=TRUE),
max(., na.rm=TRUE))) %>%
tidyr::gather() %>%
dplyr::mutate(id=str_sub(key, 1, -5),
stat=str_sub(key, -3)) %>%
dplyr::select(-key) %>%
tidyr::spread(stat, value)
# sav_ranges_trn_df <- ranges_trn_df; all.equal(sav_ranges_trn_df, ranges_trn_df)
# sav_ranges_new_df <- ranges_new_df; all.equal(sav_ranges_new_df, ranges_new_df)
get_ranges_df <- function(obs_df, feats, class_var) {
ranges_df <- obs_df[, c(class_var, feats)] %>%
dplyr::group_by_(class_var) %>%
dplyr::summarise_each(funs(min(., na.rm=TRUE),
max(., na.rm=TRUE))) %>%
tidyr::gather(key, value, -1) %>%
mutate(id=str_sub(key, 1, -5),
stat.vname=paste0(str_sub(key, -3), ".", class_var)) %>%
unite_("stat.class", c("stat.vname", class_var), sep=".") %>%
dplyr::select(-key) %>%
spread(stat.class, value)
return(ranges_df)
}
rsp_var_out_OOB <- paste0(glb_rsp_var_out, glb_sel_mdl_id)
rsp_var_out_new <- paste0(glb_rsp_var_out, glb_fin_mdl_id)
ranges_trn_df <- get_ranges_df(obs_df=glb_trnobs_df, feats=tmp_feats_df$id,
class_var=glb_rsp_var)
ranges_fit_df <- get_ranges_df(obs_df=glb_fitobs_df, feats=tmp_feats_df$id,
class_var=glb_rsp_var)
ranges_OOB_df <- get_ranges_df(obs_df=glb_OOBobs_df, feats=tmp_feats_df$id,
class_var=rsp_var_out_OOB)
ranges_new_df <- get_ranges_df(obs_df=glb_newobs_df, feats=tmp_feats_df$id,
class_var=rsp_var_out_new)
for (obsset in c("OOB", "new")) {
if (obsset == "OOB") {
ranges_ref_df <- ranges_fit_df; obs_df <- glb_OOBobs_df;
rsp_var_out_obs <- rsp_var_out_OOB; sprintf_pfx <- "OOBobs";
} else {
ranges_ref_df <- ranges_trn_df; obs_df <- glb_newobs_df;
rsp_var_out_obs <- rsp_var_out_new; sprintf_pfx <- "newobs";
}
plt_feats_df <- glb_feats_df %>%
merge(ranges_all_df, all=TRUE) %>%
merge(ranges_ref_df, all=TRUE) %>%
merge(ranges_OOB_df, all=TRUE) %>%
merge(ranges_new_df, all=TRUE) %>%
subset(!is.na(min) & (id != ".rnorm"))
row.names(plt_feats_df) <- plt_feats_df$id
range_outlier_ids <- c(NULL)
for (clss in unique(obs_df[, rsp_var_out_obs])) {
for (stat in c("min", "max")) {
if (stat == "min") {
dsp_feats <- plt_feats_df[
which(plt_feats_df[, paste("min", rsp_var_out_obs, clss, sep=".")] <
plt_feats_df[, paste("min", glb_rsp_var, clss, sep=".")]), "id"]
} else {
dsp_feats <- plt_feats_df[
which(plt_feats_df[, paste("max", rsp_var_out_obs, clss, sep=".")] >
plt_feats_df[, paste("max", glb_rsp_var, clss, sep=".")]), "id"]
}
if (length(dsp_feats) > 0) {
ths_ids <- c(NULL)
for (feat in dsp_feats) {
if (stat == "min") {
ths_ids <- union(ths_ids,
obs_df[(obs_df[, rsp_var_out_obs] == clss) &
(obs_df[, feat] <
plt_feats_df[plt_feats_df$id == feat, paste("min", glb_rsp_var, clss, sep=".")]),
glb_id_var])
} else {
ths_ids <- union(ths_ids,
obs_df[(obs_df[, rsp_var_out_obs] == clss) &
(obs_df[, feat] >
plt_feats_df[plt_feats_df$id == feat, paste("max", glb_rsp_var, clss, sep=".")]),
glb_id_var])
}
}
tmp_obs_df <- obs_df[obs_df[, glb_id_var] %in% ths_ids,
c(glb_id_var, rsp_var_out_obs, dsp_feats)]
if (stat == "min") {
print(sprintf("%s %s %s: min < min of Train range: %d",
sprintf_pfx, rsp_var_out_obs, clss, nrow(tmp_obs_df)))
} else {
print(sprintf("%s %s %s: max > max of Train range: %d",
sprintf_pfx, rsp_var_out_obs, clss, nrow(tmp_obs_df)))
}
myprint_df(tmp_obs_df)
print(subset(plt_feats_df, id %in% dsp_feats))
range_outlier_ids <- union(range_outlier_ids, ths_ids)
}
}
}
print(sprintf("%s total range outliers: %d", sprintf_pfx, length(range_outlier_ids)))
}
}
out_fname <- paste0(glb_out_pfx, "out.csv")
write.csv(obsout_df, out_fname, quote=FALSE, row.names=FALSE)
#cat(" ", "\n", file=submit_fn, append=TRUE)
# print(orderBy(~ -max.auc.OOB, glb_models_df[, c("id",
# "max.auc.OOB", "max.Accuracy.OOB")]))
for (txt_var in glb_txt_vars) {
# Print post-stem-words but need post-stop-words for debugging ?
print(sprintf(" All post-stem-words term weights for %s:", txt_var))
myprint_df(glb_post_stem_words_terms_df_lst[[txt_var]])
terms_mtrx <- glb_post_stem_words_terms_mtrx_lst[[txt_var]]
print(glb_allobs_df[
which(terms_mtrx[, tail(glb_post_stem_words_terms_df_lst[[txt_var]], 1)$pos] > 0),
c(glb_id_var, glb_txt_vars)])
print(nrow(subset(glb_post_stem_words_terms_df_lst[[txt_var]], freq == 1)))
#print(glb_allobs_df[which(terms_mtrx[, 207] > 0), c(glb_id_var, glb_txt_vars)])
#unlist(strsplit(glb_allobs_df[2157, "description"], ""))
#glb_allobs_df[2442, c(glb_id_var, glb_txt_vars)]
#terms_mtrx[2442, terms_mtrx[2442, ] > 0]
print(sprintf(" All post-stem-words term freq distribution for %s:", txt_var))
print(table(glb_post_stem_words_terms_df_lst[[txt_var]]$freq))
print(sprintf(" All post-stem-words term length distribution for %s:", txt_var))
print(table(nchar(glb_post_stem_words_terms_df_lst[[txt_var]]$term)))
print(subset(glb_post_stem_words_terms_df_lst[[txt_var]], nchar(term) >= 10))
print(sprintf(" Analyzed term weights for %s:", txt_var))
tmp_df <- glb_post_stem_words_terms_df_lst[[txt_var]]
anl_terms_vctr <- union(select_terms, assoc_terms)
print(subset(tmp_df, term %in% anl_terms_vctr))
# tmp_freq1_df <- subset(tmp_df, freq == 1)
# tmp_freq1_df$top_n <- grepl(paste0(top_n_vctr, collapse="|"), tmp_freq1_df$term)
# print(subset(tmp_freq1_df, top_n == TRUE))
}## [1] " All post-stem-words term weights for descr.my:"
## term weight freq pos cor.y cor.y.abs
## condit condit 104.99189 162 40 -0.066484434 0.066484434
## use use 85.98543 129 168 0.047049552 0.047049552
## new new 66.17058 54 103 0.019384025 0.019384025
## screen screen 62.95334 86 140 -0.106844601 0.106844601
## scratch scratch 62.68763 105 139 0.006098563 0.006098563
## good good 60.85429 66 78 -0.096553969 0.096553969
## weight.(-0.352,1.3] weight.(1.3,2.95] weight.(-2,-0.352]
## condit 26.29079 75.31182 3.389277
## use 14.09108 70.44050 1.453840
## new 13.80850 50.11853 2.243548
## screen 27.79115 33.63425 1.527939
## scratch 9.95466 50.87938 1.853589
## good 19.20397 38.63705 3.013269
## term weight freq pos cor.y cor.y.abs
## crack crack 38.371043 37 46 -0.050824015 0.050824015
## clean clean 21.802549 13 37 -0.063126049 0.063126049
## digit digit 13.590475 8 53 -0.051295956 0.051295956
## devic devic 11.050366 12 52 0.019713747 0.019713747
## lcd lcd 5.589801 4 88 0.004202773 0.004202773
## chip chip 4.415348 4 36 -0.010040896 0.010040896
## weight.(-0.352,1.3] weight.(1.3,2.95] weight.(-2,-0.352]
## crack 15.331243 23.039800 0.0000000
## clean 10.401573 11.400976 0.0000000
## digit 9.483549 4.106926 0.0000000
## devic 1.902006 8.543176 0.6051837
## lcd 0.000000 5.589801 0.0000000
## chip 1.177426 3.237922 0.0000000
## term weight freq pos cor.y cor.y.abs
## addit addit 3.280736 3 7 -0.07056423 0.07056423
## 2016 2016 3.174241 3 3 -0.04994372 0.04994372
## warranti warranti 3.174241 3 170 -0.04994372 0.04994372
## batteri batteri 3.095541 3 21 -0.05635477 0.05635477
## complet complet 2.830180 3 39 0.02862448 0.02862448
## associ associ 2.789484 3 17 0.01970363 0.01970363
## weight.(-0.352,1.3] weight.(1.3,2.95] weight.(-2,-0.352]
## addit 2.044019 1.2367172 0
## 2016 3.174241 0.0000000 0
## warranti 3.174241 0.0000000 0
## batteri 2.308539 0.7870019 0
## complet 0.000000 2.8301798 0
## associ 0.000000 2.7894844 0
## UniqueID
## 163 10327
## 703 11478
## 730 11621
## descr.my
## 163 Used - Comes with charger. In Excellent Condition
## 703
## 730 Stopped charging a few years ago, took to apple and they said the connector pin inside the ipad is
## [1] 0
## [1] " All post-stem-words term freq distribution for descr.my:"
##
## 3 4 5 6 7 8 9 10 11 12 13 14 16 17 18 19 20 21
## 39 26 12 7 16 8 5 5 2 7 6 5 1 2 2 2 1 1
## 22 24 25 30 32 34 35 36 37 39 42 44 47 54 56 66 73 76
## 1 1 4 4 2 1 1 1 1 1 1 1 1 2 1 1 1 1
## 86 105 129 162
## 1 1 1 1
## [1] " All post-stem-words term length distribution for descr.my:"
##
## 2 3 4 5 6 7 8 9
## 5 22 48 42 29 20 8 3
## [1] term weight freq
## [4] pos cor.y cor.y.abs
## [7] weight.(-0.352,1.3] weight.(1.3,2.95] weight.(-2,-0.352]
## <0 rows> (or 0-length row.names)
## [1] " Analyzed term weights for descr.my:"
## term weight freq pos cor.y cor.y.abs
## condit condit 104.99189 162 40 -0.066484434 0.066484434
## use use 85.98543 129 168 0.047049552 0.047049552
## new new 66.17058 54 103 0.019384025 0.019384025
## screen screen 62.95334 86 140 -0.106844601 0.106844601
## scratch scratch 62.68763 105 139 0.006098563 0.006098563
## good good 60.85429 66 78 -0.096553969 0.096553969
## great great 59.35965 54 79 -0.040803661 0.040803661
## excel excel 55.03968 47 62 -0.029091369 0.029091369
## work work 53.59832 73 175 0.010821559 0.010821559
## no no 48.77032 76 106 -0.072260421 0.072260421
## function function 44.64560 56 73 -0.220756841 0.220756841
## like like 38.45622 25 91 0.037087200 0.037087200
## crack crack 38.37104 37 46 -0.050824015 0.050824015
## fulli fulli 37.86149 36 72 -0.180294633 0.180294633
## box box 33.89934 34 27 -0.108671366 0.108671366
## mint mint 32.48445 19 99 0.064975099 0.064975099
## item item 32.07093 44 85 -0.036605482 0.036605482
## lock lock 30.06550 25 92 0.012596569 0.012596569
## see see 29.82147 30 142 -0.036163793 0.036163793
## icloud icloud 28.80859 20 82 -0.007105792 0.007105792
## perfect perfect 18.79950 18 121 -0.002582106 0.002582106
## weight.(-0.352,1.3] weight.(1.3,2.95] weight.(-2,-0.352]
## condit 26.290793 75.31182 3.389277
## use 14.091084 70.44050 1.453840
## new 13.808503 50.11853 2.243548
## screen 27.791149 33.63425 1.527939
## scratch 9.954660 50.87938 1.853589
## good 19.203970 38.63705 3.013269
## great 12.830957 43.61208 2.916612
## excel 10.031396 40.32089 4.687394
## work 11.593822 40.82569 1.178810
## no 11.747595 33.33125 3.691476
## function 28.552319 12.98905 3.104240
## like 5.598127 30.05903 2.799063
## crack 15.331243 23.03980 0.000000
## fulli 23.597147 11.52082 2.743522
## box 11.237672 21.63076 1.030904
## mint 4.995046 27.48941 0.000000
## item 8.565115 21.59279 1.913021
## lock 8.334989 21.73051 0.000000
## see 11.002570 16.86270 1.956201
## icloud 12.225384 16.58320 0.000000
## perfect 1.771017 14.80207 2.226421if (glb_is_classification && glb_is_binomial)
print(glb_models_df[glb_models_df$id == glb_sel_mdl_id,
"opt.prob.threshold.OOB"])
print(sprintf("glb_sel_mdl_id: %s", glb_sel_mdl_id))## [1] "glb_sel_mdl_id: Ensemble.glmnet"print(sprintf("glb_fin_mdl_id: %s", glb_fin_mdl_id))## [1] "glb_fin_mdl_id: Final.Ensemble.glmnet"get_dsp_models_df()## [1] "Cross Validation issues:"## Warning in get_dsp_models_df(): Cross Validation issues:## MFO.lm Max.cor.Y.rcv.1X1.glmnet
## 0 0
## Max.cor.Y.rcv.1X1.cp.0.rpart RFE.X.gbm
## 0 1
## RFE.X.bagEarth
## 1## id min.RMSE.fit
## Ensemble.glmnet Ensemble.glmnet 0.2210626
## RFE.X.center.scale.rf RFE.X.center.scale.rf 0.4791840
## RFE.X.YeoJohnson.rf RFE.X.YeoJohnson.rf 0.4794695
## RFE.X.rf RFE.X.rf 0.4813558
## RFE.X.gbm RFE.X.gbm 0.5057258
## RFE.X.spatialSign.rf RFE.X.spatialSign.rf 0.5078949
## RFE.X.rpart RFE.X.rpart 0.5186586
## Max.cor.Y.rpart Max.cor.Y.rpart 0.5425538
## Max.cor.Y.rcv.1X1.cp.0.rpart Max.cor.Y.rcv.1X1.cp.0.rpart 0.5448116
## RFE.X.glmnet RFE.X.glmnet 0.6164891
## All.X.glmnet All.X.glmnet 0.6260497
## RFE.X.Interact.glmnet RFE.X.Interact.glmnet 0.6277709
## RFE.X.svmLinear RFE.X.svmLinear 0.6324697
## RFE.X.earth RFE.X.earth 0.6332435
## RFE.X.svmLinear2 RFE.X.svmLinear2 0.6335311
## RFE.X.glm RFE.X.glm 0.6340075
## Low.cor.X.glmnet Low.cor.X.glmnet 0.6377835
## RFE.X.bagEarth RFE.X.bagEarth 0.6379618
## Max.cor.Y.rcv.1X1.glmnet Max.cor.Y.rcv.1X1.glmnet 0.6473136
## Max.cor.Y.rcv.5X5.glmnet Max.cor.Y.rcv.5X5.glmnet 0.6474622
## Max.cor.Y.rcv.5X3.glmnet Max.cor.Y.rcv.5X3.glmnet 0.6479740
## Max.cor.Y.rcv.3X1.glmnet Max.cor.Y.rcv.3X1.glmnet 0.6483723
## Max.cor.Y.rcv.5X1.glmnet Max.cor.Y.rcv.5X1.glmnet 0.6486277
## Max.cor.Y.rcv.3X3.glmnet Max.cor.Y.rcv.3X3.glmnet 0.6488737
## Max.cor.Y.rcv.3X5.glmnet Max.cor.Y.rcv.3X5.glmnet 0.6491051
## Interact.High.cor.Y.glmnet Interact.High.cor.Y.glmnet 0.6508063
## RFE.X.avNNet RFE.X.avNNet 0.9285701
## RFE.X.nnet RFE.X.nnet 0.9287422
## MFO.lm MFO.lm 0.9364496
## max.Adj.R.sq.fit max.R.sq.fit
## Ensemble.glmnet 0.9458537464 0.947076404
## RFE.X.center.scale.rf 0.8875717665 0.897182567
## RFE.X.YeoJohnson.rf 0.8872183523 0.896859364
## RFE.X.rf 0.8875717665 0.897182567
## RFE.X.gbm 0.7693114426 0.789031593
## RFE.X.spatialSign.rf 0.9184073331 0.925382190
## RFE.X.rpart NA 0.738383665
## Max.cor.Y.rpart NA 0.661469678
## Max.cor.Y.rcv.1X1.cp.0.rpart NA 0.662176564
## RFE.X.glmnet 0.5940573881 0.628758934
## All.X.glmnet 0.4974337085 0.620643509
## RFE.X.Interact.glmnet 0.5471574216 0.631152416
## RFE.X.svmLinear 0.5690145632 0.575965941
## RFE.X.earth 0.5852497794 0.620704234
## RFE.X.svmLinear2 0.5702700433 0.577201172
## RFE.X.glm 0.6213993720 0.645825219
## Low.cor.X.glmnet 0.4713008397 0.576187931
## RFE.X.bagEarth 0.8127487129 0.828755678
## Max.cor.Y.rcv.1X1.glmnet 0.5215575671 0.523100930
## Max.cor.Y.rcv.5X5.glmnet 0.5216616597 0.523204687
## Max.cor.Y.rcv.5X3.glmnet 0.5216616597 0.523204687
## Max.cor.Y.rcv.3X1.glmnet 0.5216600038 0.523203036
## Max.cor.Y.rcv.5X1.glmnet 0.5216616597 0.523204687
## Max.cor.Y.rcv.3X3.glmnet 0.5216626630 0.523205687
## Max.cor.Y.rcv.3X5.glmnet 0.5216626630 0.523205687
## Interact.High.cor.Y.glmnet 0.5166396765 0.523656197
## RFE.X.avNNet 0.3487472073 0.359251285
## RFE.X.nnet 0.3001577246 0.359982951
## MFO.lm 0.0003064874 0.001918896if (glb_is_regression) {
print(sprintf("%s OOB RMSE: %0.4f", glb_sel_mdl_id,
glb_models_df[glb_models_df$id == glb_sel_mdl_id, "min.RMSE.OOB"]))
if (!is.null(glb_category_var)) {
#stop(here"); glb_to_sav(); glb_ctgry_df <- sav_ctgry_df
# OOB_ctgry_df <- myget_category_stats(glb_OOBobs_df, glb_sel_mdl_id, "OOB")
# glb_ctgry_df <- merge(glb_ctgry_df, subset(OOB_ctgry_df, select=-.n.OOB),
# by=glb_category_var, all=TRUE)
#
# #glb_fitobs_df <- glb_get_predictions(glb_fitobs_df, glb_sel_mdl_id, glb_rsp_var_out)
# glb_ctgry_df <- merge(glb_ctgry_df,
# myget_category_stats(obs_df=glb_fitobs_df, mdl_id=glb_sel_mdl_id, label="fit"),
# by=glb_category_var, all=TRUE)
# row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_trnobs_df, mdl_id=glb_fin_mdl_id, label="trn"),
by=glb_category_var, all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
glb_ctgry_df <- merge(glb_ctgry_df,
myget_category_stats(obs_df=glb_newobs_df, mdl_id=glb_fin_mdl_id, label="new"),
by=glb_category_var, all=TRUE)
row.names(glb_ctgry_df) <- glb_ctgry_df[, glb_category_var]
if (any(grepl("OOB", glb_model_evl_criteria)))
print(orderBy(~-err.abs.OOB.mean, glb_ctgry_df)) else
print(orderBy(~-err.abs.fit.mean, glb_ctgry_df))
print(colSums(glb_ctgry_df[, -grep(glb_category_var, names(glb_ctgry_df))]))
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
pred_stats_df <-
mypredict_mdl(mdl=glb_models_lst[[glb_fin_mdl_id]],
df=glb_newobs_df,
rsp_var=glb_rsp_var,
rsp_var_out=glb_rsp_var_out,
mdl_id=glb_fin_mdl_id,
label="new",
model_summaryFunction=glb_sel_mdl$control$summaryFunction,
model_metric=glb_sel_mdl$metric,
model_metric_maximize=glb_sel_mdl$maximize,
ret_type="stats")
print(sprintf("%s prediction stats for glb_newobs_df:", glb_fin_mdl_id))
print(pred_stats_df)
}
}## [1] "Ensemble.glmnet OOB RMSE: 1.0406"
## prdl.descr.my.fctr .n.Tst .n.OOB.x .freqRatio.Tst
## iPadmini3#1 iPadmini3#1 5 5 0.008726003
## iPad3#0 iPad3#0 27 27 0.047120419
## iPadmini2#1 iPadmini2#1 18 19 0.031413613
## iPadAir#0 iPadAir#0 29 29 0.050610820
## iPadAir2#0 iPadAir2#0 32 32 0.055846422
## iPadAir#1 iPadAir#1 20 20 0.034904014
## Unknown#0 Unknown#0 34 34 0.059336824
## iPad3#1 iPad3#1 10 11 0.017452007
## iPad4#1 iPad4#1 25 25 0.043630017
## iPadmini2#0 iPadmini2#0 28 29 0.048865620
## iPadmini3#0 iPadmini3#0 21 22 0.036649215
## iPad2#0 iPad2#0 68 71 0.118673647
## Unknown#1 Unknown#1 32 32 0.055846422
## iPadmini#1 iPadmini#1 34 35 0.059336824
## iPad1#1 iPad1#1 30 31 0.052356021
## iPad4#0 iPad4#0 20 21 0.034904014
## iPadAir2#1 iPadAir2#1 9 9 0.015706806
## iPadmini#0 iPadmini#0 62 63 0.108202443
## iPad1#0 iPad1#0 42 43 0.073298429
## iPad2#1 iPad2#1 27 32 0.047120419
## .freqRatio.OOB err.abs.fit.sum err.abs.fit.mean .n.fit
## iPadmini3#1 0.008474576 1.006188 0.33539603 3
## iPad3#0 0.045762712 10.534039 0.24497764 43
## iPadmini2#1 0.032203390 2.464464 0.22404221 11
## iPadAir#0 0.049152542 6.906065 0.20927469 33
## iPadAir2#0 0.054237288 6.534992 0.18671406 35
## iPadAir#1 0.033898305 4.416322 0.15772578 28
## Unknown#0 0.057627119 4.773216 0.14916300 32
## iPad3#1 0.018644068 2.430834 0.12793862 19
## iPad4#1 0.042372881 2.025938 0.12662115 16
## iPadmini2#0 0.049152542 2.627509 0.12511945 21
## iPadmini3#0 0.037288136 1.731655 0.12368964 14
## iPad2#0 0.120338983 6.961378 0.10877153 64
## Unknown#1 0.054237288 2.594689 0.10811205 24
## iPadmini#1 0.059322034 4.102736 0.10519837 39
## iPad1#1 0.052542373 4.069636 0.10174089 40
## iPad4#0 0.035593220 2.666249 0.09522319 28
## iPadAir2#1 0.015254237 1.476058 0.09225365 16
## iPadmini#0 0.106779661 4.928832 0.08497986 58
## iPad1#0 0.072881356 4.323815 0.08007064 54
## iPad2#1 0.054237288 3.249884 0.07557870 43
## err.abs.OOB.sum err.abs.OOB.mean .n.OOB.y err.abs.trn.sum
## iPadmini3#1 7.685565 1.5371130 5 1.006188
## iPad3#0 13.130682 0.4863216 27 10.534039
## iPadmini2#1 9.406429 0.4950752 19 3.130255
## iPadAir#0 19.008714 0.6554729 29 6.906065
## iPadAir2#0 17.224364 0.5382614 32 6.534992
## iPadAir#1 12.156288 0.6078144 20 4.416322
## Unknown#0 21.872608 0.6433120 34 4.773216
## iPad3#1 7.360733 0.6691575 11 6.355604
## iPad4#1 15.475946 0.6190379 25 2.025938
## iPadmini2#0 14.331345 0.4941843 29 6.285601
## iPadmini3#0 13.593255 0.6178752 22 6.107344
## iPad2#0 40.268731 0.5671652 71 17.225443
## Unknown#1 15.703961 0.4907488 32 2.594689
## iPadmini#1 25.720908 0.7348831 35 7.185693
## iPad1#1 15.224456 0.4911115 31 7.785961
## iPad4#0 17.721903 0.8439001 21 6.674761
## iPadAir2#1 2.199368 0.2443742 9 1.476058
## iPadmini#0 30.029142 0.4766531 63 8.689872
## iPad1#0 27.125496 0.6308255 43 7.963236
## iPad2#1 35.951318 1.1234787 32 22.041583
## err.abs.trn.mean .n.trn err.abs.new.sum err.abs.new.mean
## iPadmini3#1 0.33539603 3 7.685565 1.5371130
## iPad3#0 0.24497764 43 13.130682 0.4863216
## iPadmini2#1 0.26085461 12 8.740638 0.4855910
## iPadAir#0 0.20927469 33 19.008714 0.6554729
## iPadAir2#0 0.18671406 35 17.224364 0.5382614
## iPadAir#1 0.15772578 28 12.156288 0.6078144
## Unknown#0 0.14916300 32 21.872608 0.6433120
## iPad3#1 0.31778022 20 3.435962 0.3435962
## iPad4#1 0.12662115 16 15.475946 0.6190379
## iPadmini2#0 0.28570912 22 10.673253 0.3811876
## iPadmini3#0 0.40715625 15 9.217567 0.4389317
## iPad2#0 0.25709617 67 30.004666 0.4412451
## Unknown#1 0.10811205 24 15.703961 0.4907488
## iPadmini#1 0.17964233 40 22.637951 0.6658221
## iPad1#1 0.18990149 41 11.508130 0.3836043
## iPad4#0 0.23016418 29 13.713390 0.6856695
## iPadAir2#1 0.09225365 16 2.199368 0.2443742
## iPadmini#0 0.14728597 59 26.268102 0.4236791
## iPad1#0 0.14478610 55 23.486075 0.5591923
## iPad2#1 0.45919964 48 17.159619 0.6355415
## .n.new
## iPadmini3#1 5
## iPad3#0 27
## iPadmini2#1 18
## iPadAir#0 29
## iPadAir2#0 32
## iPadAir#1 20
## Unknown#0 34
## iPad3#1 10
## iPad4#1 25
## iPadmini2#0 28
## iPadmini3#0 21
## iPad2#0 68
## Unknown#1 32
## iPadmini#1 34
## iPad1#1 30
## iPad4#0 20
## iPadAir2#1 9
## iPadmini#0 62
## iPad1#0 42
## iPad2#1 27
## .n.Tst .n.OOB.x .freqRatio.Tst .freqRatio.OOB
## 573.000000 590.000000 1.000000 1.000000
## err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## 79.824499 2.862591 621.000000 361.191213
## err.abs.OOB.mean .n.OOB.y err.abs.trn.sum err.abs.trn.mean
## 12.966766 590.000000 139.712861 4.489814
## .n.trn err.abs.new.sum err.abs.new.mean .n.new
## 638.000000 301.302851 11.266517 573.000000
## [1] "Final.Ensemble.glmnet prediction stats for glb_newobs_df:"
## id max.R.sq.new min.RMSE.new max.Adj.R.sq.new
## 1 Final.Ensemble.glmnet 0.3180219 0.8539244 0.3009113if (glb_is_classification) {
print(sprintf("%s OOB confusion matrix & accuracy: ", glb_sel_mdl_id))
print(t(confusionMatrix(glb_OOBobs_df[, paste0(glb_rsp_var_out, glb_sel_mdl_id)],
glb_OOBobs_df[, glb_rsp_var])$table))
if (!is.null(glb_category_var)) {
tmp_OOBobs_df <- glb_OOBobs_df[, c(glb_category_var, predct_accurate_var_name)]
names(tmp_OOBobs_df)[length(names(tmp_OOBobs_df))] <- "accurate.OOB"
aOOB_ctgry_df <- mycreate_xtab_df(tmp_OOBobs_df, names(tmp_OOBobs_df))
aOOB_ctgry_df[is.na(aOOB_ctgry_df)] <- 0
aOOB_ctgry_df <- mutate(aOOB_ctgry_df,
.n.OOB = accurate.OOB.FALSE + accurate.OOB.TRUE,
max.accuracy.OOB = accurate.OOB.TRUE / .n.OOB)
#intersect(names(glb_ctgry_df), names(aOOB_ctgry_df))
glb_ctgry_df <- merge(glb_ctgry_df, aOOB_ctgry_df, all=TRUE)
print(dsp_ctgry_df <- orderBy(~-accurate.OOB.FALSE, glb_ctgry_df))
print("Top category OOB errors:")
print(glb_OOBobs_df[(glb_OOBobs_df[, glb_category_var] ==
dsp_ctgry_df[1, glb_category_var]) &
!glb_OOBobs_df[, predct_accurate_var_name],
c(glb_id_var, glb_rsp_var_raw, paste0(glb_rsp_var_out, glb_sel_mdl_id),
glb_category_var,
row.names(head(myget_feats_importance(glb_sel_mdl), 5)),
# "biddable", "startprice", "condition",
glb_txt_vars)])
}
if ((glb_rsp_var %in% names(glb_newobs_df)) &&
!(any(is.na(glb_newobs_df[, glb_rsp_var])))) {
print(sprintf("%s new confusion matrix & accuracy: ", glb_fin_mdl_id))
print(t(confusionMatrix(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)],
glb_newobs_df[, glb_rsp_var])$table))
}
}
if (!is.null(glb_featsimp_df))
print(orderBy(as.formula(paste0("~ -", glb_sel_mdl_id, ".importance")),
subset(glb_featsimp_df, importance > 10)))## Ensemble.glmnet.importance
## startprice.log10.predict.RFE.X.spatialSign.rf 100.00000
## startprice.log10.predict.RFE.X.rf 39.53565
## startprice.log10.predict.RFE.X.YeoJohnson.rf 31.42341
## startprice.log10.predict.RFE.X.center.scale.rf 24.23239
## startprice.log10.predict.RFE.X.earth 22.94697
## startprice.log10.predict.RFE.X.avNNet 22.37479
## startprice.log10.predict.RFE.X.glm 22.37479
## startprice.log10.predict.RFE.X.glmnet 22.37479
## startprice.log10.predict.RFE.X.svmLinear 22.37331
## startprice.log10.predict.RFE.X.gbm 19.93481
## startprice.log10.predict.RFE.X.bagEarth 18.94024
## startprice.log10.predict.RFE.X.svmLinear2 16.76393
## startprice.log10.predict.RFE.X.nnet 14.50780
## importance
## startprice.log10.predict.RFE.X.spatialSign.rf 100.00000
## startprice.log10.predict.RFE.X.rf 39.53565
## startprice.log10.predict.RFE.X.YeoJohnson.rf 31.42341
## startprice.log10.predict.RFE.X.center.scale.rf 24.23239
## startprice.log10.predict.RFE.X.earth 22.94697
## startprice.log10.predict.RFE.X.avNNet 22.37479
## startprice.log10.predict.RFE.X.glm 22.37479
## startprice.log10.predict.RFE.X.glmnet 22.37479
## startprice.log10.predict.RFE.X.svmLinear 22.37331
## startprice.log10.predict.RFE.X.gbm 19.93481
## startprice.log10.predict.RFE.X.bagEarth 18.94024
## startprice.log10.predict.RFE.X.svmLinear2 16.76393
## startprice.log10.predict.RFE.X.nnet 14.50780
## Final.Ensemble.glmnet.importance
## startprice.log10.predict.RFE.X.spatialSign.rf 100.00000
## startprice.log10.predict.RFE.X.rf 39.53565
## startprice.log10.predict.RFE.X.YeoJohnson.rf 31.42341
## startprice.log10.predict.RFE.X.center.scale.rf 24.23239
## startprice.log10.predict.RFE.X.earth 22.94697
## startprice.log10.predict.RFE.X.avNNet 22.37479
## startprice.log10.predict.RFE.X.glm 22.37479
## startprice.log10.predict.RFE.X.glmnet 22.37479
## startprice.log10.predict.RFE.X.svmLinear 22.37331
## startprice.log10.predict.RFE.X.gbm 19.93481
## startprice.log10.predict.RFE.X.bagEarth 18.94024
## startprice.log10.predict.RFE.X.svmLinear2 16.76393
## startprice.log10.predict.RFE.X.nnet 14.50780print("glb_newobs_df prediction stats:")## [1] "glb_newobs_df prediction stats:"print(myplot_histogram(glb_newobs_df, paste0(glb_rsp_var_out, glb_fin_mdl_id)))## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
if (glb_is_classification)
print(table(glb_newobs_df[, paste0(glb_rsp_var_out, glb_fin_mdl_id)]))
# Use this to see how prediction changes by changing one or more values
# players_df <- data.frame(id=c("Chavez", "Giambi", "Menechino", "Myers", "Pena"),
# OBP=c(0.338, 0.391, 0.369, 0.313, 0.361),
# SLG=c(0.540, 0.450, 0.374, 0.447, 0.500),
# cost=c(1400000, 1065000, 295000, 800000, 300000))
# players_df$RS.predict <- predict(glb_models_lst[[csm_mdl_id]], players_df)
# print(orderBy(~ -RS.predict, players_df))
# dsp_chisq.test(Headline.contains="[Vi]deo")
if ((length(diff <- setdiff(names(glb_trnobs_df), names(glb_allobs_df))) > 0) ||
(length(diff <- setdiff(names(glb_fitobs_df), names(glb_allobs_df))) > 0) ||
(length(diff <- setdiff(names(glb_OOBobs_df), names(glb_allobs_df))) > 0) ||
(length(diff <- setdiff(names(glb_newobs_df), names(glb_allobs_df))) > 0)) {
print(diff)
stop("glb_*obs_df not in sync")
}
if (glb_save_envir)
save(glb_feats_df, glb_allobs_df,
#glb_trnobs_df, glb_fitobs_df, glb_OOBobs_df, glb_newobs_df,
glb_models_df, dsp_models_df, glb_models_lst, glb_model_type,
glb_sel_mdl, glb_sel_mdl_id,
glb_fin_mdl, glb_fin_mdl_id,
file=paste0(glb_out_pfx, "prdnew_dsk.RData"))
sav_fin_mdl <- glb_fin_mdl; sav_sel_mdl <- glb_sel_mdl
save(sav_fin_mdl, sav_sel_mdl, file=paste0(glb_out_pfx, "sav_mdl.RData"))
# load(file=paste0(glb_out_pfx, "sav_mdl_01.RData"), verbose=TRUE)
# prv_fin_mdl <- sav_fin_mdl; prv_sel_mdl <- sav_sel_mdl
# load(file=paste0(glb_out_pfx, "sav_mdl.RData"), verbose=TRUE)
# cur_fin_mdl <- sav_fin_mdl; cur_sel_mdl <- sav_sel_mdl
# all.equal(cur_fin_mdl, prv_fin_mdl)
# cur_fitobs_df <- cur_fin_mdl$trainingData; prv_fitobs_df <- prv_fin_mdl$trainingData; all.equal(cur_fitobs_df, prv_fitobs_df)
# nrow(cur_fitobs_df); nrow(prv_fitobs_df)
# names(cur_fitobs_df); names(prv_fitobs_df)
# all.equal(cur_fin_mdl$bestTune, prv_fin_mdl$bestTune)
# all.equal(glb_sel_mdl, sav_sel_mdl)
# cur_fitobs_df <- cur_sel_mdl$trainingData; prv_fitobs_df <- prv_sel_mdl$trainingData; all.equal(cur_fitobs_df, prv_fitobs_df)
# head(myget_feats_importance(glb_sel_mdl)); head(myget_feats_importance(sav_sel_mdl))
# head(myget_feats_importance(cur_sel_mdl)); head(myget_feats_importance(prv_sel_mdl))
# tmp_replay_lst <- replay.petrisim(pn=glb_analytics_pn,
# replay.trans=(glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
# "data.new.prediction")), flip_coord=TRUE)
# print(ggplot.petrinet(tmp_replay_lst[["pn"]]) + coord_flip())
glb_chunks_df <- myadd_chunk(glb_chunks_df, "display.session.info", major.inc=TRUE)## label step_major step_minor label_minor bgn end
## 16 predict.data.new 9 0 0 475.546 539.131
## 17 display.session.info 10 0 0 539.131 NA
## elapsed
## 16 63.585
## 17 NANull Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 11 fit.models 7 1 1 156.490
## 16 predict.data.new 9 0 0 475.546
## 12 fit.models 7 2 2 414.728
## 10 fit.models 7 0 0 117.870
## 9 select.features 6 0 0 81.578
## 5 extract.features 3 0 0 32.411
## 7 manage.missing.data 4 1 1 63.861
## 1 import.data 1 0 0 9.070
## 2 inspect.data 2 0 0 20.630
## 15 fit.data.training 8 1 1 467.205
## 13 fit.models 7 3 3 460.952
## 8 partition.data.training 5 0 0 76.419
## 3 scrub.data 2 1 1 29.214
## 6 cluster.data 4 0 0 61.919
## 4 transform.data 2 2 2 31.942
## 14 fit.data.training 8 0 0 466.771
## end elapsed duration
## 11 414.728 258.238 258.238
## 16 539.131 63.585 63.585
## 12 460.951 46.223 46.223
## 10 156.489 38.619 38.619
## 9 117.869 36.291 36.291
## 5 61.918 29.507 29.507
## 7 76.419 12.558 12.558
## 1 20.630 11.560 11.560
## 2 29.214 8.584 8.584
## 15 475.545 8.340 8.340
## 13 466.770 5.819 5.818
## 8 81.578 5.159 5.159
## 3 31.942 2.728 2.728
## 6 63.860 1.941 1.941
## 4 32.411 0.469 0.469
## 14 467.204 0.433 0.433
## [1] "Total Elapsed Time: 539.131 secs"